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files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +index.html \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/.travis.yml b/third_party/AnyBimanual/third_party/RLBench/.travis.yml new file mode 100644 index 0000000000000000000000000000000000000000..42ec2bccef192cc457cc8e7367c6a58a1ea83220 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/.travis.yml @@ -0,0 +1,59 @@ +dist: xenial +language: python +services: + - xvfb +python: + - "3.6" + +env: + # Will kick of 2 builds. One for units and one for tasks. + - TEST_DIR=unit + - TEST_DIR=demos +before_install: + - sudo apt-get update -qq + - sudo apt-get install qtbase5-dev qtdeclarative5-dev libqt5webkit5-dev libsqlite3-dev + - sudo apt-get install qt5-default qttools5-dev-tools +install: + - cd ../ + - cur=`pwd` + # Pull V-REP + - wget http://www.coppeliarobotics.com/files/CoppeliaSim_Edu_V4_1_0_Ubuntu16_04.tar.xz + - tar -xf CoppeliaSim_Edu_V4_1_0_Ubuntu16_04.tar.xz + - export COPPELIASIM_ROOT="$cur/CoppeliaSim_Edu_V4_1_0_Ubuntu16_04" + - echo $COPPELIASIM_ROOT + - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$COPPELIASIM_ROOT:$COPPELIASIM_ROOT/platforms + - export QT_QPA_PLATFORM_PLUGIN_PATH=$COPPELIASIM_ROOT + + # Install PyRep + - git clone https://github.com/stepjam/PyRep.git + - cd PyRep + - pip3 install -r requirements.txt + - pip3 install . + + # Gym needed for some of unit tests + - pip3 install gym + + # Go back to RLBench repo + - cd ../RLBench + - pip3 install -r requirements.txt +# command to run tests +script: + # Runs unit tests and generates reports (110 min timeout) + - travis_wait 110 python3 travisci_run_tests.py $TEST_DIR $TRAVIS_BRANCH $TRAVIS_BUILD_NUMBER +after_script: + # Get previously generated index file + - git clone --quiet --branch=gh-pages https://${GITHUB_TOKEN}@github.com/stepjam/RLBench.git gh-pages + - cd gh-pages + + # Generated html files will be one directory above + - cp -r ../reports . + # Move script that will amend the index + - mv ../travisci_generate_index.py . + + # Generated new index.html + - python3 travisci_generate_index.py $TEST_DIR $TRAVIS_BRANCH $TRAVIS_BUILD_NUMBER $TRAVIS_TEST_RESULT + + # Push generate html to gh-pages branch + - git add index.html reports/ + - git commit -m "Travis build $TRAVIS_BUILD_NUMBER" + - git push -fq origin gh-pages diff --git a/third_party/AnyBimanual/third_party/RLBench/LICENSE b/third_party/AnyBimanual/third_party/RLBench/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d54744fb2cdcbda4328a9ed1d20583e0abefc77d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/LICENSE @@ -0,0 +1,190 @@ +RLBENCH: THE ROBOT LEARNING BENCHMARK & LEARNING ENVIRONMENT + +LICENCE AGREEMENT + +WE (Imperial College of Science, Technology and Medicine, (“Imperial College London”)) +ARE WILLING TO LICENSE THIS SOFTWARE TO YOU (a licensee “You”) ONLY ON THE +CONDITION THAT YOU ACCEPT ALL OF THE TERMS CONTAINED IN THE +FOLLOWING AGREEMENT. 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IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/third_party/AnyBimanual/third_party/RLBench/README.md b/third_party/AnyBimanual/third_party/RLBench/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8f5c5c39a30f26047b4d4a1613c195cebd9cb032 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/README.md @@ -0,0 +1,434 @@ +# RLBench: Robot Learning Benchmark [![Unit Tests](https://github.com/stepjam/RLBench/workflows/Unit%20Tests/badge.svg)](https://github.com/stepjam/RLBench/actions) [![Task Tests](https://github.com/stepjam/RLBench/workflows/Task%20Tests/badge.svg)](https://github.com/stepjam/RLBench/actions) [![Discord](https://img.shields.io/discord/694945190867370155.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https://discord.gg/DXPCjmd) + +![task grid image missing](readme_files/task_grid.png) + +## Modifications + This is my fork of RLBench. Modifications include: +- 6 new tasks, bug fixes, and extensions of existing tasks. See Appendix A in the [paper](https://peract.github.io/) for details. +- Data generation also records if `ignore_collisions` was used with a waypoint for motion-planning. +- `data_generator.py` supports an "all_variations" setting that samples from all possible task variations. + +I branched off `master` in Feb 2022, so this fork is not up to date with the latest changes in the official repo. + +## RLBench + +**RLBench** is an ambitious large-scale benchmark and learning environment +designed to facilitate research in a number of vision-guided manipulation +research areas, including: reinforcement learning, imitation learning, +multi-task learning, geometric computer vision, and in particular, +few-shot learning. [Click here for website and paper.](https://sites.google.com/corp/view/rlbench) + +**Contents:** +- [Announcements](#announcements) +- [Install](#install) +- [Running Headless](#running-headless) +- [Getting Started](#getting-started) + - [Few-Shot Learning and Meta Learning](#few-shot-learning-and-meta-learning) + - [Reinforcement Learning](#reinforcement-learning) + - [Sim-to-Real](#sim-to-real) + - [Imitation Learning](#imitation-learning) + - [Multi-Task Learning](#multi-task-learning) + - [RLBench Gym](#rlbench-gym) + - [Swapping Arms](#swapping-arms) +- [Tasks](#tasks) +- [Task Building](#task-building) +- [Gotchas!](#gotchas) +- [Contributing](#contributing) +- [Acknowledgements](#acknowledgements) +- [Citation](#citation) + +## Announcements + +### 11 May 2022 + +- Shaped rewards added for: **reach_target** and **take_lid_off_saucepan**. Pass `shaped_rewards=True` to `Environement` class + +### 18 February 2022 + +- **Version 1.2.0 is live!** Note: This release will cause code-breaking API changes for action modes. + +### 1 July 2021 + +- New instructions on headless GPU rendering [here](#running-headless)! + +### 8 September 2020 + +- New tutorial series on task creation [here](https://www.youtube.com/watch?v=bKaK_9O3v7Y&list=PLsffAlO5lBTRiBwnkw2-x0U7t6TrNCkfc)! + +### 1 April 2020 + +- We added a Discord channel to allow the RLBench community to help one another. Click the Discord badge above. + +### 28 January 2020 + +- RLBench has been accepted to RA-L with presentation at ICRA! +- Ability to easily swap out arms added. [See here](#swapping-arms). + +### 17 December 2019 + +- Gym is now supported! + + +## Install + +RLBench is built around PyRep and V-REP. First head to the +[PyRep github](https://github.com/stepjam/PyRep) page and install. + +**If you previously had PyRep installed, you will need to update your installation!** + +Hopefully you have now installed PyRep and have run one of the PyRep examples. +Now lets install RLBench: + +```bash +pip install -r requirements.txt +pip install . +``` +Or you can install directly via pip +```bash +pip install git+https://github.com/stepjam/RLBench.git +``` + +And that's it! + +## Running Headless + +If you are running on a machine without display (i.e. Cloud VMs, compute clusters), +you can refer to the following guide to run RLBench headlessly with rendering. + +### Initial setup + +First, configure your X config. This should only be done once to set up. + +```bash +sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024 +echo -e 'Section "ServerFlags"\n\tOption "MaxClients" "2048"\nEndSection\n' \ + | sudo tee /etc/X11/xorg.conf.d/99-maxclients.conf +``` + +Leave out `--use-display-device=None` if the GPU is headless, i.e. if it has no display outputs. + +### Running X + +Then, whenever you want to run RLBench, spin up X. + +```bash +# nohup and disown is important for the X server to keep running in the background +sudo nohup X :99 & disown +``` + +Test if your display works using glxgears. + +```bash +DISPLAY=:99 glxgears +``` + +If you have multiple GPUs, you can select your GPU by doing the following. + +```bash +DISPLAY=:99. glxgears +``` + +### Running X without sudo + +To spin up X with non-sudo users, edit file '/etc/X11/Xwrapper.config' and replace line: + +``` +allowed_users=console +``` +with lines: +``` +allowed_users=anybody +needs_root_rights=yes +``` +If the file does not exist already, you can create it. + +## Getting Started + +The benchmark places particular emphasis on few-shot learning and meta learning +due to breadth of tasks available, though it can be used in numerous ways. Before using RLBench, +checkout the [Gotchas](#gotchas) section. + +### Few-Shot Learning and Meta Learning + +We have created splits of tasks called 'Task Sets', which consist of a +collection of X training tasks and 5 tests tasks. Here X can be 10, 25, 50, or 95. +For example, to work on the task set with 10 training tasks, we import `FS10_V1`: + +```python +import numpy as np +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.environment import Environment +from rlbench.tasks import FS10_V1 + +action_mode = MoveArmThenGripper( + arm_action_mode=JointVelocity(), + gripper_action_mode=Discrete() +) +env = Environment(action_mode) +env.launch() + +train_tasks = FS10_V1['train'] +test_tasks = FS10_V1['test'] +task_to_train = np.random.choice(train_tasks, 1)[0] +task = env.get_task(task_to_train) +task.sample_variation() # random variation +descriptions, obs = task.reset() +obs, reward, terminate = task.step(np.random.normal(size=env.action_shape)) +``` + +A full example can be seen in [examples/few_shot_rl.py](examples/few_shot_rl.py). + +### Reinforcement Learning + +```python +import numpy as np +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.environment import Environment +from rlbench.tasks import ReachTarget + +action_mode = MoveArmThenGripper( + arm_action_mode=JointVelocity(), + gripper_action_mode=Discrete() +) +env = Environment(action_mode) +env.launch() + +task = env.get_task(ReachTarget) +descriptions, obs = task.reset() +obs, reward, terminate = task.step(np.random.normal(size=env.action_shape)) +``` + +A full example can be seen in [examples/single_task_rl.py](examples/single_task_rl.py). +If you would like to bootstrap from demonstrations, then take a look at [examples/single_task_rl_with_demos.py](examples/single_task_rl_with_demos.py). + + +### Sim-to-Real + +```python +import numpy as np +from rlbench import Environment +from rlbench import RandomizeEvery +from rlbench import VisualRandomizationConfig +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.tasks import OpenDoor + +# We will borrow some from the tests dir +rand_config = VisualRandomizationConfig( + image_directory='../tests/unit/assets/textures') + +action_mode = MoveArmThenGripper( + arm_action_mode=JointVelocity(), + gripper_action_mode=Discrete() +) +env = Environment( + action_mode, randomize_every=RandomizeEvery.EPISODE, + frequency=1, visual_randomization_config=rand_config) + +env.launch() + +task = env.get_task(OpenDoor) +descriptions, obs = task.reset() +obs, reward, terminate = task.step(np.random.normal(size=env.action_shape)) +``` + +A full example can be seen in [examples/single_task_rl_domain_randomization.py](examples/single_task_rl_domain_randomization.py). + +### Imitation Learning + +```python +import numpy as np +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.environment import Environment +from rlbench.tasks import ReachTarget + +# To use 'saved' demos, set the path below +DATASET = 'PATH/TO/YOUR/DATASET' + +action_mode = MoveArmThenGripper( + arm_action_mode=JointVelocity(), + gripper_action_mode=Discrete() +) +env = Environment(action_mode, DATASET) +env.launch() + +task = env.get_task(ReachTarget) + +demos = task.get_demos(2) # -> List[List[Observation]] +demos = np.array(demos).flatten() + +batch = np.random.choice(demos, replace=False) +batch_images = [obs.left_shoulder_rgb for obs in batch] +predicted_actions = predict_action(batch_images) +ground_truth_actions = [obs.joint_velocities for obs in batch] +loss = behaviour_cloning_loss(ground_truth_actions, predicted_actions) + +``` + +A full example can be seen in [examples/imitation_learning.py](examples/imitation_learning.py). + +### Multi-Task Learning + +We have created splits of tasks called 'Task Sets', which consist of a +collection of X training tasks. Here X can be 15, 30, 55, or 100. +For example, to work on the task set with 15 training tasks, we import `MT15_V1`: + +```python +import numpy as np +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.environment import Environment +from rlbench.tasks import MT15_V1 + +action_mode = MoveArmThenGripper( + arm_action_mode=JointVelocity(), + gripper_action_mode=Discrete() +) +env = Environment(action_mode) +env.launch() + +train_tasks = MT15_V1['train'] +task_to_train = np.random.choice(train_tasks, 1)[0] +task = env.get_task(task_to_train) +task.sample_variation() # random variation +descriptions, obs = task.reset() +obs, reward, terminate = task.step(np.random.normal(size=env.action_shape)) +``` + +A full example can be seen in [examples/multi_task_learning.py](examples/multi_task_learning.py). + +### RLBench Gym + +RLBench is __Gym__ compatible! Ensure you have gym installed (`pip3 install gym`). + +Simply select your task of interest from [rlbench/tasks/](rlbench/tasks/), and +then load the task by using the task name (e.g. 'reach_target') followed by +the observation mode: 'state' or 'vision'. + +```python +import gym +import rlbench.gym + +env = gym.make('reach_target-state-v0') +# Alternatively, for vision: +# env = gym.make('reach_target-vision-v0') + +training_steps = 120 +episode_length = 40 +for i in range(training_steps): + if i % episode_length == 0: + print('Reset Episode') + obs = env.reset() + obs, reward, terminate, _ = env.step(env.action_space.sample()) + env.render() # Note: rendering increases step time. + +print('Done') +env.close() +``` + +A full example can be seen in [examples/rlbench_gym.py](examples/rlbench_gym.py). + +### Swapping Arms + +The default Franka Panda Arm _can_ be swapped out for another. This can be +useful for those who have custom tasks or want to perform sim-to-real +experiments on the tasks. However, if you swap out the arm, then we can't +guarantee that the task will be solvable. +For example, the Mico arm has a very small workspace in comparison to the +Franka. + +**For benchmarking, the arm should remain as the Franka Panda.** + +Currently supported arms: + +- Franka Panda arm with Franka gripper `(franka)` +- Mico arm with Mico gripper `(mico)` +- Jaco arm with 3-finger Jaco gripper `(jaco)` +- Sawyer arm with Baxter gripper `(sawyer)` +- UR5 arm with Robotiq 85 gripper `(ur5)` + +You can then swap out the arm using `robot_configuration`: + +```python +env = Environment(action_mode=action_mode, robot_setup='sawyer') +``` + +A full example (using the Sawyer) can be seen in [examples/swap_arm.py](examples/swap_arm.py). + +_Don't see the arm that you want to use?_ Your first step is to make sure it is +in PyRep, and if not, then you can follow the instructions for importing new +arm on the PyRep GitHub page. After that, feel free to open an issue and +we can being it in to RLBench for you. + +## Tasks + +To see a full list of all tasks, [see here](rlbench/tasks). + +To see gifs of each of the tasks, [see here](https://drive.google.com/drive/folders/1TqbulbbCEqVBd6SBHatphFlUK2JQLkYu?usp=sharing). + +## Task Building + +The task building tool is the interface for users who wish to create new tasks +to be added to the RLBench task repository. Each task has 2 associated files: +a V-REP model file (_.ttm_), which holds all of the scene information and demo +waypoints, and a python (_.py_) file, which is responsible for wiring the +scene objects to the RLBench backend, applying variations, defining success +criteria, and adding other more complex task behaviours. + +Video tutorial series [here](https://www.youtube.com/watch?v=bKaK_9O3v7Y&list=PLsffAlO5lBTRiBwnkw2-x0U7t6TrNCkfc)! + +In-depth text tutorials: +- [Simple Task](tutorials/simple_task.md) +- [Complex Task](tutorials/complex_task.md) + +## Gotchas! + +- **Using low-dimensional task observations (rather than images):** RLBench was designed to be challenging, putting emphasis on vision rather than +toy-based low dimensional inputs. Although each task does supply a low-dimensional +output this should be used with extreme caution! + - Why? Imagine you are training a reinforcement learning agent to pick up a block; halfway through + training, the block slips from the gripper and falls of the table. These low-dimensional values + will now be out of distribution. I.e. RLBench does not safeguard against objects going out of the + workspace. This issue does not arise when using image-based observations. + +- **Using non-standard image size:** RLBench by default uses image observation sizes of 128x128. +When using an alternative size, be aware that you may need to collect your saved demonstrations again. + - Why? If we instead specify a 64x64 image observation size to the `ObservationConfig` then the + scene cameras will now render to that size. However, the saved demos on disk will now be **resized** + to be 64x64. + This resizing will of course mean that small artifacts may be present in stored demos + that may not be present in the 'live' observations from the scene. Instead, prefer to re-collect demos + using the image observation sized you plan to use in the 'live' environment. + + +## Contributing + +New tasks using our task building tool, in addition to bug fixes, are very +welcome! When building your task, please ensure that you run the task validator +in the task building tool. + +A full contribution guide is coming soon! + +## Acknowledgements + +Models were supplied from turbosquid.com, cgtrader.com, free3d.com, +thingiverse.com, and cadnav.com. + +## Citation + +``` +@article{james2019rlbench, + title={RLBench: The Robot Learning Benchmark \& Learning Environment}, + author={James, Stephen and Ma, Zicong and Rovick Arrojo, David and Davison, Andrew J.}, + journal={IEEE Robotics and Automation Letters}, + year={2020} +} +``` diff --git a/third_party/AnyBimanual/third_party/RLBench/pyproject.toml b/third_party/AnyBimanual/third_party/RLBench/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..eb60a5df4e883bde07a17a467062c3a1a6d675c4 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/pyproject.toml @@ -0,0 +1,35 @@ +[tool.poetry] +name = "rlbench" +version = "1.2.1" +description = "RLBench" +authors = [ 'Stephen James ', +'Mohit Shridhar ', +'Markus Grotz '] +packages = [{include = 'rlbench'}] + +include=['*.ttm', '*.obj', '**/**/*.ttm', '**/**/*.obj', 'task_design.ttt'] + +readme = "README.md" +classifiers = [ + "Programming Language :: Python :: 3", + "Framework :: Robot Framework " +] + +[tool.poetry.dependencies] +python = ">=3.8,<4.0" +numpy = "^1.23.3" +Pillow = "^9.2.0" +pyquaternion = "^0.9.9" +click-prompt = "^0.5.1" +rich-click = "^1.8.3" + +[tool.poetry.dev-dependencies] +toml = "^0.10.2" + +[tool.poetry.extras] +docs = ["sphinx"] + +[build-system] +requires = ["setuptools", "wheel", "poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + diff --git a/third_party/AnyBimanual/third_party/RLBench/requirements.txt b/third_party/AnyBimanual/third_party/RLBench/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..2848bc939018127f076e3a7afc4216dcba9f9bed --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/requirements.txt @@ -0,0 +1,6 @@ +numpy +Pillow +pyquaternion +html-testRunner +setuptools +natsort diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/__init__.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a7b11f5143eb09128c1ff0aa1f7bb3cebdcda431 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/__init__.py @@ -0,0 +1,17 @@ +__version__ = '1.2.0' + +import numpy as np +import pyrep + +pr_v = np.array(pyrep.__version__.split('.'), dtype=int) +if pr_v.size < 4 or np.any(pr_v < np.array([4, 1, 0, 2])): + raise ImportError( + 'PyRep version must be greater than 4.1.0.2. Please update PyRep.') + + +from rlbench.environment import Environment +from rlbench.action_modes.action_mode import ActionMode, ArmActionMode, GripperActionMode +from rlbench.observation_config import ObservationConfig +from rlbench.observation_config import CameraConfig +from rlbench.sim2real.domain_randomization import RandomizeEvery +from rlbench.sim2real.domain_randomization import VisualRandomizationConfig diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/chest_of_drawers.ttm b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/chest_of_drawers.ttm new file mode 100644 index 0000000000000000000000000000000000000000..a162dedff4ee1225e869dca7505e2e918c9a7a49 Binary files /dev/null and b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/chest_of_drawers.ttm differ diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/dish_rack.ttm b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/dish_rack.ttm new file mode 100644 index 0000000000000000000000000000000000000000..2d31ca280d54461403e1f3c540bf772841e92ecf Binary files /dev/null and b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/dish_rack.ttm differ diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/large_container.ttm b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/large_container.ttm new file mode 100644 index 0000000000000000000000000000000000000000..97ca4516ae656e62dcc4be9fd85effd5ec3ebf26 Binary files /dev/null and b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/large_container.ttm differ diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/plate.ttm b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/plate.ttm new file mode 100644 index 0000000000000000000000000000000000000000..ff1a994f969f3c177d0dab726781053fdf55746f Binary files /dev/null and b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/plate.ttm differ diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942.obj new file mode 100644 index 0000000000000000000000000000000000000000..9037895c7f72b35c3a5423b2c06338c123c27ca1 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942.obj @@ -0,0 +1,300 @@ +# Blender v2.69 (sub 0) OBJ File: '' +# www.blender.org +mtllib 942.mtl +o 942_Cube.3808 +v -0.110239 2.267619 0.127450 +v -0.110614 -3.361886 -0.429501 +v -0.111386 -3.344613 -0.242160 +v -0.111404 2.353482 0.163330 +v -0.111998 2.335518 0.217394 +v -0.983510 2.099804 0.725786 +v -0.109808 2.630890 -0.421631 +v -3.078341 -5.268859 -0.377662 +v -0.111511 -3.392331 -0.131821 +v -0.110459 2.478650 0.426319 +v -2.919201 3.903470 -1.348350 +v 0.057815 -3.171742 -0.416638 +v -0.050692 2.684151 -0.416410 +v -3.328391 -5.018465 0.735875 +v 7.319371 0.546132 -2.131820 +v -0.102997 2.721291 0.436404 +v 1.025137 11.436226 8.483665 +v 2.016927 2.766350 -1.348908 +v 0.113425 2.755595 -0.438170 +v 0.110472 -3.156270 -0.425318 +v -1.938558 -7.663589 0.688434 +v -0.112882 -4.039399 0.240807 +v 7.029951 0.029315 -3.108818 +v -0.113109 -4.472351 0.424855 +v -0.086511 -4.419592 0.433780 +v 0.049706 2.710952 0.418510 +v -0.732327 13.504180 6.556881 +v 4.087878 11.846714 6.220618 +v 0.394693 5.111168 -3.139631 +v 0.110103 -3.223746 -0.175544 +v 0.110010 2.579742 -0.345766 +v 0.108585 2.528642 -0.189950 +v -1.720560 -7.254683 1.730318 +v -0.112960 -5.939967 -0.186330 +v 0.040135 -6.022398 -0.144731 +v -0.110849 -5.416361 -0.437421 +v 8.532151 -2.055889 -1.081586 +v 0.111152 -4.587684 0.420017 +v 0.111031 2.689697 0.428276 +v -0.055610 8.354084 0.707764 +v -0.061197 8.404687 0.704748 +v -0.182329 9.296103 1.818218 +v -0.359956 9.392921 1.958036 +v -0.838489 8.801117 1.904645 +v -0.375443 8.068656 0.665690 +v -0.770417 8.707601 1.728268 +v 2.338619 14.089819 4.537641 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+vt 0.928242 0.495922 +vt 0.940199 0.464933 +vt 0.938532 0.464779 +vt 0.967724 0.038786 +vt 0.937614 0.496466 +vt 0.937887 0.494819 +vt 0.967724 0.618355 +vt 0.968009 0.617615 +vt 0.968009 0.904762 +vt 0.877393 1.707294 +vt 0.875837 0.228250 +vt 0.814201 1.232648 +vt 0.815666 1.261061 +vt 0.816266 1.264855 +vt 0.783949 1.498971 +vt 0.821991 1.232358 +vt 0.818255 1.232650 +vt 0.785504 0.019921 +vt 0.823091 1.264349 +vt 0.928846 0.000000 +vt 0.823019 0.000000 +vt 0.517246 0.544797 +vt 0.523685 0.539685 +vt 0.591053 0.787743 +vt 0.516079 0.545976 +vt 0.592420 0.411091 +vt 0.783949 0.383108 +vt 0.391977 0.440588 +vt 0.586222 0.000000 +vt 0.547624 0.417348 +vt 0.621261 0.321127 +vt 0.612265 0.328759 +vt 0.558663 0.372642 +vt 0.547736 0.416544 +vt 0.604406 0.387920 +vt 0.968508 0.491240 +vt 0.968666 0.490438 +vt 0.968666 0.904619 +vt 0.582633 1.047018 +vt 0.392401 0.683372 +vt 0.190664 0.000000 +vt 0.391977 0.401022 +vt 0.192534 0.757950 +vt 0.138924 0.609440 +vt 0.967724 0.491484 +vt 0.968508 0.490438 +vt 0.968508 0.617614 +vt 0.140982 0.618029 +vt 0.147618 0.638988 +vt 0.136817 0.643131 +vt 0.138151 0.642927 +vt 0.140111 0.642460 +vt 0.147542 0.640124 +vt 0.122929 0.614039 +vt 0.000424 0.363639 +vt 0.194241 1.047012 +vt 0.391977 0.663913 +vt 0.968058 0.966295 +vt 0.967724 0.960495 +vt 0.968058 0.957003 +vt 0.107782 0.617913 +vt 0.116860 0.616061 +vt 0.117134 0.648651 +vt 0.115002 0.640413 +vt 0.000000 0.606425 +vt 0.783949 0.645996 +vt 0.968991 0.162229 +vt 0.972682 0.293341 +vt 0.972242 0.295467 +vt 0.968855 0.163038 +vt 0.967724 0.116994 +vt 0.972682 0.087969 +vt 0.516208 0.543825 +vt 0.969994 0.411425 +vt 0.969994 0.490438 +vt 0.967724 0.480297 +vt 0.981053 0.087969 +vt 0.967724 0.020297 +vt 0.988912 0.069275 +vt 0.988706 0.081070 +vt 0.988912 0.000000 +vt 0.621751 0.310836 +vt 0.560945 0.343390 +usemtl 942_mtl +s off +f 1/1 2/2 3/3 4/4 +f 1/1 7/5 2/2 +f 9/6 22/7 24/8 10/9 +f 10/9 5/10 9/6 +f 4/4 3/3 9/6 5/10 +f 53/11 52/12 36/13 +f 53/11 34/14 52/12 +f 19/15 20/16 12/17 13/18 +f 12/17 2/19 7/20 13/18 +f 54/21 53/22 56/23 55/24 +f 53/22 36/25 56/23 +f 60/26 48/27 30/28 32/29 +f 30/28 31/30 32/29 +f 30/28 20/31 19/32 31/30 +f 60/26 32/29 49/33 38/34 +f 49/33 39/35 38/34 +f 54/36 55/37 62/38 61/39 +f 26/40 25/41 38/42 39/43 +f 16/44 25/41 26/40 +f 10/45 24/46 25/41 16/44 +f 35/47 54/48 61/49 51/50 +f 53/51 54/48 35/47 34/52 +f 8/53 2/54 15/55 23/56 +f 20/57 15/55 2/54 12/58 +f 9/59 3/60 2/54 8/53 +f 20/57 30/61 15/55 +f 8/53 14/62 9/59 +f 21/63 51/50 61/49 +f 61/49 50/64 21/63 +f 23/65 50/64 61/49 62/66 +f 36/67 52/68 34/52 8/69 +f 8/69 23/65 36/67 +f 55/70 56/71 36/67 23/65 +f 21/63 8/69 34/52 35/47 +f 23/65 62/66 55/70 +f 35/72 51/73 21/74 +f 50/64 37/75 33/76 21/63 +f 38/77 25/78 24/79 33/80 +f 48/81 60/82 38/77 37/83 +f 33/80 37/83 38/77 +f 14/62 33/80 24/79 22/84 +f 48/81 37/83 15/55 30/61 +f 14/62 22/84 9/59 +f 14/85 8/69 21/63 33/76 +f 37/83 50/86 23/56 15/55 +f 59/87 58/88 47/89 +f 47/89 57/90 59/87 +f 42/91 27/92 47/89 58/88 +f 29/93 11/94 40/95 57/90 +f 44/96 46/97 45/98 11/94 +f 45/98 41/99 11/94 +f 11/94 27/92 44/96 +f 27/92 43/100 44/96 +f 27/92 42/91 43/100 +f 41/101 40/102 11/103 +f 47/89 29/93 57/90 +f 47/89 28/104 18/105 29/93 +f 28/106 17/107 6/108 39/109 +f 10/110 16/111 6/112 +f 26/113 39/109 6/108 16/114 +f 1/115 4/116 5/117 6/108 +f 6/108 5/117 10/118 +f 49/119 18/120 28/106 39/109 +f 11/121 6/108 17/107 27/122 +f 32/123 31/124 19/125 +f 19/126 18/120 32/127 +f 7/128 18/120 19/126 13/129 +f 18/120 49/119 32/127 +f 11/121 29/130 18/120 7/128 +f 7/128 1/115 6/108 11/121 +f 17/131 28/104 47/89 27/92 +f 41/132 65/133 57/134 40/135 +f 41/132 45/136 63/137 65/133 +f 59/87 65/138 58/88 +f 57/90 65/138 59/87 +f 43/139 64/140 44/141 +f 64/142 63/143 46/144 44/145 +f 63/143 45/146 46/144 +f 42/91 58/88 65/138 64/147 +f 64/147 43/100 42/91 +f 64/147 65/138 63/148 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942.urdf b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942.urdf new file mode 100644 index 0000000000000000000000000000000000000000..35d42feec8c21c5a0afc02c5c95af5214b28c450 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942_coll.mtl b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942_coll.mtl new file mode 100644 index 0000000000000000000000000000000000000000..70d3ba1dabe6f47379805b4d5e0070f98d01ad8b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/942/942_coll.mtl @@ -0,0 +1,10 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl None +Ns 0 +Ka 0.000000 0.000000 0.000000 +Kd 0.8 0.8 0.8 +Ks 0.8 0.8 0.8 +d 1 +illum 2 diff --git 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b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/945/945.mtl new file mode 100644 index 0000000000000000000000000000000000000000..0cbadae5207b0a8a4e8f7be4cfce327f00ef774c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/945/945.mtl @@ -0,0 +1,12 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl 945_mtl +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.052381 0.243968 0.214965 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 +map_Kd 945.png diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/945/945.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/945/945.obj new file mode 100644 index 0000000000000000000000000000000000000000..357a5355f23e53bea524a17e8660bbd33adeac36 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/945/945.obj @@ -0,0 +1,1622 @@ +# Blender 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0000000000000000000000000000000000000000..6062ebe48448234e0f52c13a82eaa03a4a2355ff --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/948/948.mtl @@ -0,0 +1,12 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl 948_mtl +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.204963 0.114069 0.412743 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 +map_Kd 948.png diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/948/948.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/948/948.obj new file mode 100644 index 0000000000000000000000000000000000000000..37376c3a0e66a42567da3f129ea35eccd46d2006 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/948/948.obj @@ -0,0 +1,5252 @@ +# Blender v2.69 (sub 0) OBJ File: '' +# www.blender.org +mtllib 948.mtl +o 948_Cube.3835 +v -0.730701 0.005585 -1.678447 +v 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b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/951/951_coll.mtl @@ -0,0 +1,10 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl None +Ns 0 +Ka 0.000000 0.000000 0.000000 +Kd 0.8 0.8 0.8 +Ks 0.8 0.8 0.8 +d 1 +illum 2 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/951/951_coll.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/951/951_coll.obj new file mode 100644 index 0000000000000000000000000000000000000000..878b1bf2ee3e901ed57f81add7fa0a50e87497d9 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/951/951_coll.obj @@ -0,0 +1,378 @@ +o convex_0 +v -0.307490 -3.823036 0.228574 +v -1.817996 -8.166092 -0.573218 +v -1.204494 -8.166092 -0.573218 +v -1.204494 -5.097534 -3.217217 +v -1.959656 -4.908927 -2.933562 +v -0.354530 -5.851964 2.070781 +v -0.307490 -4.200759 -2.036528 +v -0.307490 -7.079692 0.795367 +v -2.148537 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a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/952/952.mtl b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/952/952.mtl new file mode 100644 index 0000000000000000000000000000000000000000..0baaefd5e45e56a7d7328a1d6a2a98d04bcc2c07 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/952/952.mtl @@ -0,0 +1,12 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl 952_mtl +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.165721 0.342745 0.373049 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 +map_Kd 952.png diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/952/952.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/952/952.obj new file mode 100644 index 0000000000000000000000000000000000000000..c1e733835adb4a164d1ec059b2b8f02f245e09fb --- /dev/null +++ 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a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953.urdf b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953.urdf new file mode 100644 index 0000000000000000000000000000000000000000..bba03fdb0a6fe44e64f227373047c0d78e4fbd37 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953_coll.mtl b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953_coll.mtl new file mode 100644 index 0000000000000000000000000000000000000000..70d3ba1dabe6f47379805b4d5e0070f98d01ad8b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953_coll.mtl @@ -0,0 +1,10 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl None +Ns 0 +Ka 0.000000 0.000000 0.000000 +Kd 0.8 0.8 0.8 +Ks 0.8 0.8 0.8 +d 1 +illum 2 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953_coll.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953_coll.obj new file mode 100644 index 0000000000000000000000000000000000000000..1755206dcbcdb098f968a8b74400674dcca6741e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/953/953_coll.obj @@ -0,0 +1,108 @@ +o convex_0 +v -2.188690 -4.595634 -0.021208 +v 1.559128 -3.756504 2.327924 +v -1.573103 -3.756504 2.327924 +v 1.559128 -6.049354 2.327924 +v 1.559128 -3.756504 -2.314895 +v -1.573103 -5.993460 -2.314895 +v -1.573103 -6.049354 2.327924 +v 1.559128 -5.937566 -2.314895 +v -1.573103 -3.756504 -2.314895 +v 0.328321 -6.217517 -0.804252 +v -2.188690 -4.707421 0.313733 +v -2.188690 -4.427952 0.034237 +v -1.573103 -6.049354 -0.188906 +v 1.559128 -6.049354 1.041332 +v 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b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/956/956.mtl new file mode 100644 index 0000000000000000000000000000000000000000..02a9a3d8856ce853da83d86c6dd4767969a8493b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/956/956.mtl @@ -0,0 +1,12 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl 956_mtl +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.749681 0.092769 0.505850 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 +map_Kd 956.png diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/956/956.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/956/956.obj new file mode 100644 index 0000000000000000000000000000000000000000..6ab3fb9162e23b70939cf093dcb6f2fb6a9dce2a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/956/956.obj @@ -0,0 +1,2031 @@ +# Blender 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a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/957/957.png b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/957/957.png new file mode 100644 index 0000000000000000000000000000000000000000..d39b2674ed1de1bab58bc5f584a026d19138edaa Binary files /dev/null and b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/957/957.png differ diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/957/957.urdf b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/957/957.urdf new file mode 100644 index 0000000000000000000000000000000000000000..724d4892b31bb6fddf4fa977c4d13d7b62c0cb93 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/957/957.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git 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967.png diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/967/967.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/967/967.obj new file mode 100644 index 0000000000000000000000000000000000000000..beff84ce44438917fa686376d1e2fd45a0682365 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/967/967.obj @@ -0,0 +1,5489 @@ +# Blender v2.69 (sub 0) OBJ File: '' +# www.blender.org +mtllib 967.mtl +o 967_Cube.3910 +v -2.048887 3.474426 0.565435 +v -1.857895 4.285217 0.516395 +v -1.843449 4.814913 -0.403806 +v -1.574636 4.786903 0.470574 +v -2.169077 2.501930 0.569810 +v -1.998997 4.191433 -0.145682 +v -2.143721 -1.816087 -0.197473 +v -2.143980 -0.551070 0.008222 +v -2.185701 -2.641045 -0.318506 +v -2.305045 -3.700047 -0.464813 +v -2.592175 -4.927420 -0.638844 +v -2.080815 -2.727310 -0.568144 +v -2.197436 -3.895042 -0.631569 +v -2.373421 -4.696518 -0.722058 +v -2.150520 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150 +f 131 145 150 +f 145 141 150 +f 130 138 151 +f 127 142 151 +f 146 127 151 +f 138 146 151 +f 147 130 151 +f 147 151 152 +f 142 134 152 +f 134 147 152 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/969/969.mtl b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/969/969.mtl new file mode 100644 index 0000000000000000000000000000000000000000..d72c32e6b21c427a3be7d19ad7ca49a115446255 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/969/969.mtl @@ -0,0 +1,12 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl 969_mtl +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.410559 0.696906 0.302616 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 +map_Kd 969.png diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/969/969.obj 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a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/985/985.urdf b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/985/985.urdf new file mode 100644 index 0000000000000000000000000000000000000000..6848e08d10bc2268c407ae0977ebe1065263b09b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/985/985.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/985/985_coll.mtl b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/985/985_coll.mtl new file mode 100644 index 0000000000000000000000000000000000000000..70d3ba1dabe6f47379805b4d5e0070f98d01ad8b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/985/985_coll.mtl @@ -0,0 +1,10 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl None 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b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/986/986.mtl new file mode 100644 index 0000000000000000000000000000000000000000..fa7c3ee4f14f3fb21f5d8dd0bf5aa351b93ca95b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/986/986.mtl @@ -0,0 +1,12 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl 986_mtl +Ns 96.078431 +Ka 0.000000 0.000000 0.000000 +Kd 0.620565 0.458965 0.199377 +Ks 0.500000 0.500000 0.500000 +Ni 1.000000 +d 1.000000 +illum 2 +map_Kd 986.png diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/986/986.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/986/986.obj new file mode 100644 index 0000000000000000000000000000000000000000..09ef78c1b878842380e01ceeba577992e05ff93c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/986/986.obj @@ -0,0 +1,5332 @@ +# Blender 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index 0000000000000000000000000000000000000000..90cd0569c7e1d8345c88f394afef0c73e5e445fd --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/987/987.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/987/987_coll.mtl b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/987/987_coll.mtl new file mode 100644 index 0000000000000000000000000000000000000000..70d3ba1dabe6f47379805b4d5e0070f98d01ad8b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/987/987_coll.mtl @@ -0,0 +1,10 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl None +Ns 0 +Ka 0.000000 0.000000 0.000000 +Kd 0.8 0.8 0.8 +Ks 0.8 0.8 0.8 +d 1 +illum 2 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/987/987_coll.obj 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b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/989/989.urdf new file mode 100644 index 0000000000000000000000000000000000000000..36bf81f1855c51e45053495e7688216d5c0f7ba8 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/989/989.urdf @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/989/989_coll.mtl b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/989/989_coll.mtl new file mode 100644 index 0000000000000000000000000000000000000000..70d3ba1dabe6f47379805b4d5e0070f98d01ad8b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/989/989_coll.mtl @@ -0,0 +1,10 @@ +# Blender MTL File: 'None' +# Material Count: 1 + +newmtl None +Ns 0 +Ka 0.000000 0.000000 0.000000 +Kd 0.8 0.8 0.8 +Ks 0.8 0.8 0.8 +d 1 +illum 2 diff --git 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a/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/990/990.obj b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/990/990.obj new file mode 100644 index 0000000000000000000000000000000000000000..71bd09c265da9a21a089e82b0f26104f4b47e6e4 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/assets/procedural_objects/990/990.obj @@ -0,0 +1,5189 @@ +# Blender v2.69 (sub 0) OBJ File: '' +# www.blender.org +mtllib 990.mtl +o 990_Cube.3994 +v -0.825111 -1.515198 -0.047415 +v -0.812564 -0.771606 -0.037480 +v -0.851255 -2.425049 -0.164100 +v -0.887412 -3.302124 -0.465801 +v -0.876709 -3.018860 -0.276456 +v -0.901653 -3.447350 -0.369811 +v -0.950562 -3.277817 -0.109595 +v -0.797958 0.987671 0.371463 +v -0.843324 -2.363216 -0.278867 +v -0.887558 -2.916321 -0.597633 +v -0.854639 -1.971359 -0.495458 +v -0.750348 2.792908 -0.192539 +v -0.628344 4.161255 -0.108623 +v -0.788204 1.516754 -0.308394 +v -0.767827 1.658066 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b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/conditions.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/conditions.py new file mode 100644 index 0000000000000000000000000000000000000000..363d2d64c7ed2f98a42e8f3f35da42dc6d33a8cc --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/conditions.py @@ -0,0 +1,194 @@ +from typing import List +import math +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.robots.end_effectors.gripper import Gripper + + +class Condition(object): + def condition_met(self): + raise NotImplementedError() + + def reset(self): + # Used if the conditions store any state. + pass + + +class ColorCondition(object): + def _init_(self, shape: Shape, success_rgb: List[float]): + self.shape = shape + self.success_rgb = success_rgb + + def condition_met(self): + obj_rgb = self.shape.get_color() + met = (obj_rgb == self.success_rgb) + return met, False + + +class JointCondition(Condition): + def __init__(self, joint: Joint, position: float): + """in radians if revoloute, or meters if prismatic""" + self._joint = joint + self._original_pos = joint.get_joint_position() + self._pos = position + + def condition_met(self): + met = math.fabs( + self._joint.get_joint_position() - self._original_pos) > self._pos + return met, False + + +class DetectedCondition(Condition): + def __init__(self, obj: Object, detector: ProximitySensor, + negated: bool = False): + self._obj = obj + self._detector = detector + self._negated = negated + + def condition_met(self): + met = self._detector.is_detected(self._obj) + if self._negated: + met = not met + return met, False + + +class NothingGrasped(Condition): + def __init__(self, gripper: Gripper): + self._gripper = gripper + + def condition_met(self): + met = len(self._gripper.get_grasped_objects()) == 0 + return met, False + + +class GraspedCondition(Condition): + def __init__(self, gripper: Gripper, object: Object): + self._gripper = gripper + self._object_handle = object.get_handle() + + def condition_met(self): + met = len([ob for ob in self._gripper.get_grasped_objects() + if self._object_handle == ob.get_handle()]) > 0 + return met, False + + +class DetectedSeveralCondition(Condition): + def __init__(self, objects: List[Object], detector: ProximitySensor, + number_needed: int): + self._objects = objects + self._detector = detector + self._number_needed = number_needed + + def condition_met(self): + count = 0 + for ob in self._objects: + if self._detector.is_detected(ob): + count += 1 + met = False + if count >= self._number_needed: + met = True + return met, False + + +class EmptyCondition(Condition): + + def __init__(self, container: list): + self._container = container + + def condition_met(self): + met = len(self._container) == 0 + return met, False + + +class FollowCondition(Condition): + + def __init__(self, obj: Object, points: list, + relative_to: Object = None, delta_limit: float = 0.01, + start_after_first: bool = True): + self._obj = obj + self._ponts = points + self._relative_to = relative_to + self._delta_limit = delta_limit + self._start_after_first = start_after_first + self._index = 0 + self._strikes = 0 + + def condition_met(self): + pos = self._obj.get_position(self._relative_to) + first = True + for i in range(self._index, len(self._ponts)): + p = self._ponts[i] + dist = math.sqrt((pos[0] - p[0]) ** 2 + + (pos[1] - p[1]) ** 2 + + (pos[2] - p[2]) ** 2) + # Check we aren't too far away from the next point + if dist > self._delta_limit: + # Check if we are ignoring until we reach the first point + if first and not (self._start_after_first and self._index == 0): + # If it is the first point, then we have failed + if self._strikes > 3: + return False, True + self._strikes += 1 + # Otherwise we have looped at least once and we may + # still be following the path + return False, False + # Only advance if we are super close + elif dist > self._delta_limit * 0.5: + return False, False + self._index += 1 + self._strikes = 0 + first = False + return True, False + + +class ConditionSet(Condition): + def __init__(self, conditions: List[Condition], order_matters: bool = False, + simultaneously_met: bool = True): + """alternative would be sequentially met""" + self._conditions = conditions + self._order_matters = order_matters + self._simultaneously_met = simultaneously_met # Probably wont use + self._current_condition_index = 0 + + def condition_met(self): + met = True + # term = False + if self._order_matters: + if self._current_condition_index < len(self._conditions): + for cond in self._conditions[self._current_condition_index:]: + ismet, term = cond.condition_met() + if not ismet: + break + self._current_condition_index += 1 + # Check again to see if we have now completed the order + met = self._current_condition_index >= len(self._conditions) + else: + for cond in self._conditions: + ismet, term = cond.condition_met() + met &= ismet + # if term: + # break + return met, False + + def reset(self): + self._current_condition_index = 0 + + +class OrConditions(Condition): + def __init__(self, conditions: List[Condition]): + """Logical or over all conditions""" + self._conditions = conditions + + def condition_met(self): + met = False + for cond in self._conditions: + ismet, term = cond.condition_met() + met |= ismet + if met: + break + return met, False + + def reset(self): + self._current_condition_index = 0 \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/const.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/const.py new file mode 100644 index 0000000000000000000000000000000000000000..cd3d9904e0588b23e994b1d1e8b07a4a1613b3d7 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/const.py @@ -0,0 +1,32 @@ +IMAGE_FORMAT = '%d.png' + +LEFT_SHOULDER_RGB_FOLDER = 'left_shoulder_rgb' +LEFT_SHOULDER_DEPTH_FOLDER = 'left_shoulder_depth' +LEFT_SHOULDER_MASK_FOLDER = 'left_shoulder_mask' +RIGHT_SHOULDER_RGB_FOLDER = 'right_shoulder_rgb' +RIGHT_SHOULDER_DEPTH_FOLDER = 'right_shoulder_depth' +RIGHT_SHOULDER_MASK_FOLDER = 'right_shoulder_mask' +OVERHEAD_RGB_FOLDER = 'overhead_rgb' +OVERHEAD_DEPTH_FOLDER = 'overhead_depth' +OVERHEAD_MASK_FOLDER = 'overhead_mask' +WRIST_RGB_FOLDER = 'wrist_rgb' +WRIST_DEPTH_FOLDER = 'wrist_depth' +WRIST_MASK_FOLDER = 'wrist_mask' +FRONT_RGB_FOLDER = 'front_rgb' +FRONT_DEPTH_FOLDER = 'front_depth' +FRONT_MASK_FOLDER = 'front_mask' +EPISODES_FOLDER = 'episodes' +EPISODE_FOLDER = 'episode%d' +VARIATIONS_FOLDER = 'variation%d' +VARIATIONS_ALL_FOLDER = 'all_variations' + +LOW_DIM_PICKLE = 'low_dim_obs.pkl' +VARIATION_DESCRIPTIONS = 'variation_descriptions.pkl' +VARIATION_NUMBER = 'variation_number.pkl' + +TTT_FILE = 'task_design.ttt' +BIMANUAL_TTT_FILE = 'task_design_bimanual.ttt' + + +DEPTH_SCALE = 2**24 - 1 + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/exceptions.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..4f5599e3ad7b59efad203827621ca5f1d595b09a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/exceptions.py @@ -0,0 +1,29 @@ +class BoundaryError(Exception): + """Raised when a boundary error occurs.""" + pass + + +class WaypointError(Exception): + """Raised when we place a task but cant complete it with path planning.""" + def __init__(self, message, task): + super().__init__('Error in task %s. %s' % (task.get_name(), message)) + + +class NoWaypointsError(Exception): + """Raised when there is no waypoints. So cant create demo.""" + def __init__(self, message, task): + super().__init__('Error in task %s. %s' % (task.get_name(), message)) + + +class DemoError(Exception): + """Raised when getting demo.""" + def __init__(self, message, task): + super().__init__('Error in task %s. %s' % (task.get_name(), message)) + + +class InvalidActionError(Exception): + pass + + +class TaskEnvironmentError(Exception): + pass diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/observation.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/observation.py new file mode 100644 index 0000000000000000000000000000000000000000..68822826c9bd56e12285ddede678ea8a87864e8b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/observation.py @@ -0,0 +1,105 @@ +from abc import abstractmethod +import numpy as np +from typing import List +from typing import Dict +from typing import Any + +from dataclasses import dataclass +from dataclasses import field +from enum import Enum + + +class ImageType(Enum): + RGB = 1 + DEPTH = 2 + MASK = 3 + +@dataclass +class CameraImage: + + name: str + dtype: ImageType + data: np.ndarray + +@dataclass +class Observation: + """Storage for both visual and low-dimensional observations.""" + + #..todo:: replace + + perception_data: Dict[str, np.ndarray] + + task_low_dim_state: np.ndarray + + misc: Dict[str, Any] + + @property + @abstractmethod + def is_bimanual(self): + pass + + +#..todo:: rename to ProprioceptionObservation + +@dataclass +class UnimanualObservationData: + + joint_velocities: np.ndarray + joint_positions: np.ndarray + joint_forces: np.ndarray + gripper_open: float + gripper_pose: np.ndarray + gripper_matrix: np.ndarray + gripper_joint_positions: np.ndarray + gripper_touch_forces: np.ndarray + + ignore_collisions: np.ndarray + +@dataclass +class UnimanualObservation(UnimanualObservationData, Observation): + + + @Observation.is_bimanual.getter + def is_bimanual(self): + return False + + def get_low_dim_data(self) -> np.ndarray: + """Gets a 1D array of all the low-dimensional obseervations. + + :return: 1D array of observations. + """ + low_dim_data = [] if self.gripper_open is None else [[self.gripper_open]] + for data in [self.joint_velocities, self.joint_positions, + self.joint_forces, + self.gripper_pose, self.gripper_joint_positions, + self.gripper_touch_forces, self.task_low_dim_state]: + if data is not None: + low_dim_data.append(data) + return np.concatenate(low_dim_data) if len(low_dim_data) > 0 else np.array([]) + + +@dataclass +class BimanualObservation(Observation): + + right: UnimanualObservationData = None + left: UnimanualObservationData = None + + @Observation.is_bimanual.getter + def is_bimanual(self): + return True + + def get_low_dim_data(self, robot: UnimanualObservationData) -> np.ndarray: + """Gets a 1D array of all the low-dimensional obseervations. + + :return: 1D array of observations. + """ + low_dim_data = [] if robot.gripper_open is None else [[robot.gripper_open]] + #for data in [robot.joint_velocities, robot.joint_positions, + # robot.joint_forces, + # robot.gripper_pose, robot.gripper_joint_positions, + # robot.gripper_touch_forces, self.task_low_dim_state]: + + for data in [robot.gripper_joint_positions]: + if data is not None: + low_dim_data.append(data) + return np.concatenate(low_dim_data) if len(low_dim_data) > 0 else np.array([]) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/robot.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/robot.py new file mode 100644 index 0000000000000000000000000000000000000000..e157d2a0ecce5ca6d6572a682c303026caf2a427 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/robot.py @@ -0,0 +1,177 @@ +from pyrep.objects.object import Object +from pyrep.robots.arms.arm import Arm +from pyrep.robots.end_effectors.gripper import Gripper + +from abc import ABC +from abc import abstractmethod + +import logging + +class Robot(ABC): + """Simple container for the robot components. + """ + + @property + @abstractmethod + def is_bimanual(self): + pass + + @property + @abstractmethod + def initial_state(self): + pass + + + @abstractmethod + def release_gripper(self): + pass + + @abstractmethod + def is_in_collision(self): + pass + + @abstractmethod + def zero_velocity(self): + pass + + @abstractmethod + def actutate_gripper(self, amount: float, velocity: float, name: str): + pass + + @abstractmethod + def grasp(self, obj: Object, name: str = None): + pass + + +class UnimanualRobot(Robot): + + def __init__(self, arm: Arm, gripper: Gripper): + self.arm = arm + self.gripper = gripper + + def release_gripper(self): + self.gripper.release() + + def initial_state(self): + return [self.arm.get_configuration_tree(), self.gripper.get_configuration_tree()] + + def is_in_collision(self): + return self.arm.check_arm_collision() + + def zero_velocity(self): + self.arm.set_joint_target_velocities([0] * len(self.arm.joints)) + self.gripper.set_joint_target_velocities([0] * len(self.gripper.joints)) + + def actutate_gripper(self, amount: float, velocity: float, name: str = None): + return self.gripper.actuate(amount, velocity) + + def grasp(self, obj: Object, name: str = None): + return self.gripper.grasp(obj) + + @Robot.is_bimanual.getter + def is_bimanual(self): + return False + + +class BimanualRobot(Robot): + + def __init__(self, right_arm: Arm, right_gripper: Gripper, left_arm: Arm, left_gripper: Gripper): + self.right_arm = right_arm + self.right_gripper = right_gripper + self.left_arm = left_arm + self.left_gripper = left_gripper + + def colorize_gripper(self): + self.right_gripper.colorize([0, 0.8, 0]) + self.left_gripper.colorize([0.8, 0, 0]) + + + def hide(self, name: str = "both"): + if "both" in name: + self.right_arm.hide() + self.right_gripper.hide() + self.left_arm.hide() + self.left_gripper.hide() + elif "right" in name: + self.right_arm.hide() + self.right_gripper.hide() + elif "left" in name: + self.left_arm.hide() + self.left_gripper.hide() + + def release_gripper(self, name: str = 'both'): + if 'both' in name: + self.right_gripper.release() + self.left_gripper.release() + elif 'right' in name: + self.right_gripper.release() + elif 'left' in name: + self.left_gripper.release() + + def initial_state(self): + return [(self.right_arm.get_configuration_tree(), + self.right_gripper.get_configuration_tree()), + (self.left_arm.get_configuration_tree(), + self.left_gripper.get_configuration_tree())] + + def is_in_collision(self): + return self.right_arm.check_arm_collision() or self.left_arm.check_arm_collision() + + def zero_velocity(self): + self.right_arm.set_joint_target_velocities([0] * len(self.right_arm.joints)) + self.right_gripper.set_joint_target_velocities([0] * len(self.right_gripper.joints)) + self.left_arm.set_joint_target_velocities([0] * len(self.left_arm.joints)) + self.left_gripper.set_joint_target_velocities([0] * len(self.left_gripper.joints)) + + def get_arms_by_name(self, name: str): + if 'right' in name: + return [self.right_arm] + if 'left' in name: + return [self.left_arm] + if 'both' in name: + return [self.right_arm, self.left_arm] + + def actutate_gripper(self, amount: float, velocity: float, name: str ='both'): + logging.debug("actuating gripper for %s", name) + if 'right' in name: + return self.right_gripper.actuate(amount, velocity) + if 'left' in name: + return self.left_gripper.actuate(amount, velocity) + if 'both' in name: + right_done = self.right_gripper.actuate(amount, velocity) + left_done = self.left_gripper.actuate(amount, velocity) + return right_done and left_done + else: + logging.warning("invalid robot name %s", name) + return True + + def grasp(self, obj: Object, name: str = None): + logging.debug("grasping with %s", name) + if 'right' in name: + if obj in self.left_gripper.get_grasped_objects(): + logging.warning("Object %s is already grasped by left gripper", obj.get_name()) + return False + else: + return self.right_gripper.grasp(obj) + if 'left' in name: + if obj in self.right_gripper.get_grasped_objects(): + logging.warning("Object %s is already grasped by right gripper", obj.get_name()) + return False + else: + return self.left_gripper.grasp(obj) + if 'both' in name: + # object can be only attached to one robot + if obj in self.left_gripper.get_grasped_objects(): + right_detected = False + else: + right_detected = self.right_gripper.grasp(obj) + if obj in self.right_gripper.get_grasped_objects(): + left_detected = False + else: + left_detected = self.left_gripper.grasp(obj) + return right_detected or left_detected + + + @Robot.is_bimanual.getter + def is_bimanual(self): + return True diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/scene.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/scene.py new file mode 100644 index 0000000000000000000000000000000000000000..3898f17946c4339933b54c751a82a1355097131e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/scene.py @@ -0,0 +1,718 @@ +from typing import List, Callable + +import numpy as np +from pyrep import PyRep +from pyrep.const import ObjectType +from pyrep.errors import ConfigurationPathError +from pyrep.objects import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.vision_sensor import VisionSensor +from pyrep.robots.arms.arm import Arm +from pyrep.robots.arms.dual_panda import PandaLeft, PandaRight +from pyrep.robots.end_effectors.gripper import Gripper + +from rlbench.backend.exceptions import ( + WaypointError, BoundaryError, NoWaypointsError, DemoError) +from rlbench.backend.observation import Observation +from rlbench.backend.observation import UnimanualObservationData +from rlbench.backend.observation import UnimanualObservation +from rlbench.backend.observation import BimanualObservation + +from rlbench.backend.robot import Robot +from rlbench.backend.robot import UnimanualRobot +from rlbench.backend.robot import BimanualRobot +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task +from rlbench.backend.utils import rgb_handles_to_mask +from rlbench.demo import Demo +from rlbench.noise_model import NoiseModel +from rlbench.observation_config import ObservationConfig, CameraConfig + +STEPS_BEFORE_EPISODE_START = 10 + +import logging + + +class Scene(object): + """Controls what is currently in the vrep scene. This is used for making + sure that the tasks are easily reachable. This may be just replaced by + environment. Responsible for moving all the objects. """ + + def __init__(self, + pyrep: PyRep, + robot: Robot, + obs_config: ObservationConfig = ObservationConfig(), + robot_setup: str = 'panda'): + self.pyrep = pyrep + self.robot = robot + self.robot_setup = robot_setup + self.task = None + self._obs_config = obs_config + self._initial_task_state = None + + if self.robot.is_bimanual: + self._start_arm_joint_pos = [robot.right_arm.get_joint_positions(), robot.left_arm.get_joint_positions()] + self._starting_gripper_joint_pos = [robot.right_gripper.get_joint_positions(), robot.left_gripper.get_joint_positions()] + else: + self._start_arm_joint_pos = robot.arm.get_joint_positions() + self._starting_gripper_joint_pos = robot.gripper.get_joint_positions() + + self._workspace = Shape('workspace') + self._workspace_boundary = SpawnBoundary([self._workspace]) + + self.camera_sensors = {camera_name: VisionSensor(f"cam_{camera_name}") for camera_name, _ in self._obs_config.camera_configs.items()} + self.camera_sensors_mask = {camera_name: VisionSensor(f'cam_{camera_name}_mask') for camera_name, _ in self._obs_config.camera_configs.items()} + + + self._has_init_task = self._has_init_episode = False + self._variation_index = 0 + + # ..todo:: fixme convert to a list + if self.robot.is_bimanual: + self._initial_robot_state = [(robot.right_arm.get_configuration_tree(), + robot.right_gripper.get_configuration_tree()), + (robot.left_arm.get_configuration_tree(), + robot.left_gripper.get_configuration_tree())] + else: + self._initial_robot_state = (robot.arm.get_configuration_tree(), + robot.gripper.get_configuration_tree()) + + self._ignore_collisions_for_current_waypoint = False + + # Set camera properties from observation config + self._set_camera_properties() + + x, y, z = self._workspace.get_position() + minx, maxx, miny, maxy, _, _ = self._workspace.get_bounding_box() + self._workspace_minx = x - np.fabs(minx) - 0.2 + self._workspace_maxx = x + maxx + 0.2 + self._workspace_miny = y - np.fabs(miny) - 0.2 + self._workspace_maxy = y + maxy + 0.2 + self._workspace_minz = z + self._workspace_maxz = z + 1.0 # 1M above workspace + + self.target_workspace_check = Dummy.create() + self._step_callback = None + + if self.robot.is_bimanual: + self._robot_shapes = [self.robot.right_arm.get_objects_in_tree(object_type=ObjectType.SHAPE), + self.robot.left_arm.get_objects_in_tree(object_type=ObjectType.SHAPE)] + self._right_execute_demo_joint_position_action = None + self._left_execute_demo_joint_position_action = None + else: + self._robot_shapes = self.robot.arm.get_objects_in_tree( + object_type=ObjectType.SHAPE) + self._execute_demo_joint_position_action = None + + def load(self, task: Task) -> None: + """Loads the task and positions at the centre of the workspace. + + :param task: The task to load in the scene. + """ + task.load() # Load the task in to the scene + + # Set at the centre of the workspace + task.get_base().set_position(self._workspace.get_position()) + + self._initial_task_state = task.get_state() + self.task = task + self._initial_task_pose = task.boundary_root().get_orientation() + self._has_init_task = self._has_init_episode = False + self._variation_index = 0 + + def unload(self) -> None: + """Clears the scene. i.e. removes all tasks. """ + if self.task is not None: + self.robot.release_gripper() + if self._has_init_task: + self.task.cleanup_() + self.task.unload() + self.task = None + self._variation_index = 0 + + def init_task(self) -> None: + self.task.init_task() + self._initial_task_state = self.task.get_state() + self._has_init_task = True + self._variation_index = 0 + + def init_episode(self, index: int, randomly_place: bool=True, + max_attempts: int = 5) -> List[str]: + """Calls the task init_episode and puts randomly in the workspace. + """ + + self._variation_index = index + if not self._has_init_task: + self.init_task() + + # Try a few times to init and place in the workspace + attempts = 0 + descriptions = None + while attempts < max_attempts: + descriptions = self.task.init_episode(index) + try: + if (randomly_place and + not self.task.is_static_workspace()): + self._place_task() + if self.robot.is_in_collision(): + logging.error("robot is in collision") + raise BoundaryError() + self.task.validate() + break + except (BoundaryError, WaypointError) as e: + logging.error('Error when checking waypoints. Exception is: %s', e) + self.task.cleanup_() + self.task.restore_state(self._initial_task_state) + attempts += 1 + if attempts >= max_attempts: + raise e + + # Let objects come to rest + [self.pyrep.step() for _ in range(STEPS_BEFORE_EPISODE_START)] + self._has_init_episode = True + return descriptions + + def reset(self) -> None: + """Resets the joint angles. """ + + self.robot.release_gripper() + + if self.robot.is_bimanual: + self.reset_bimanual() + else: + self.reset_unimanual() + + self.robot.zero_velocity() + + if self.task is not None and self._has_init_task: + self.task.cleanup_() + self.task.restore_state(self._initial_task_state) + self.task.set_initial_objects_in_scene() + + def reset_unimanual(self) -> None: + arm, gripper = self._initial_robot_state + self.pyrep.set_configuration_tree(arm) + self.pyrep.set_configuration_tree(gripper) + + self.robot.arm.set_joint_positions(self._start_arm_joint_pos, disable_dynamics=True) + self.robot.gripper.set_joint_positions( + self._starting_gripper_joint_pos, disable_dynamics=True) + + + def reset_bimanual(self) -> None: + + for arm, gripper in self._initial_robot_state: + self.pyrep.set_configuration_tree(arm) + self.pyrep.set_configuration_tree(gripper) + + self.robot.right_arm.set_joint_positions(self._start_arm_joint_pos[0], disable_dynamics=True) + self.robot.right_gripper.set_joint_positions(self._starting_gripper_joint_pos[0], disable_dynamics=True) + + self.robot.left_arm.set_joint_positions(self._start_arm_joint_pos[1], disable_dynamics=True) + self.robot.left_gripper.set_joint_positions(self._starting_gripper_joint_pos[1], disable_dynamics=True) + + + def get_observation(self) -> Observation: + + observation_data = {} + perception_data = {} + + # ..todo:: extract methods + def get_rgb_depth(sensor: VisionSensor, get_rgb: bool, get_depth: bool, + get_pcd: bool, rgb_noise: NoiseModel, + depth_noise: NoiseModel, depth_in_meters: bool): + rgb = depth = pcd = None + if sensor is not None and (get_rgb or get_depth): + sensor.handle_explicitly() + if get_rgb: + rgb = sensor.capture_rgb() + if rgb_noise is not None: + rgb = rgb_noise.apply(rgb) + rgb = np.clip((rgb * 255.).astype(np.uint8), 0, 255) + if get_depth or get_pcd: + depth = sensor.capture_depth(depth_in_meters) + if depth_noise is not None: + depth = depth_noise.apply(depth) + if get_pcd: + depth_m = depth + if not depth_in_meters: + near = sensor.get_near_clipping_plane() + far = sensor.get_far_clipping_plane() + depth_m = near + depth * (far - near) + pcd = sensor.pointcloud_from_depth(depth_m) + if not get_depth: + depth = None + return rgb, depth, pcd + + def get_mask(sensor: VisionSensor, mask_fn): + mask = None + if sensor is not None: + sensor.handle_explicitly() + mask = mask_fn(sensor.capture_rgb()) + return mask + + for camera_name, camera_config in self._obs_config.camera_configs.items(): + + rgb_data, depth_data, pcd_data = get_rgb_depth(self.camera_sensors[camera_name], camera_config.rgb, camera_config.depth, camera_config.point_cloud, + camera_config.rgb_noise, camera_config.depth_noise, camera_config.depth_in_meters) + + if camera_config.mask and camera_config.masks_as_one_channel: + mask_data = get_mask(self.camera_sensors_mask[camera_name], rgb_handles_to_mask) + elif camera_config.mask: + mask_data = get_mask(self.camera_sensors_mask[camera_name], lambda x: x) + else: + mask_data = None + + perception_data.update({f'{camera_name}_rgb': rgb_data, f'{camera_name}_depth': depth_data, f'{camera_name}_point_cloud': pcd_data, + f'{camera_name}_mask': mask_data}) + + + + + def get_proprioception(arm: Arm, gripper: Gripper): + tip = arm.get_tip() + + if self._obs_config.joint_velocities: + joint_velocities=np.array(arm.get_joint_velocities()) + joint_velocities=self._obs_config.joint_velocities_noise.apply(joint_velocities) + else: + joint_velocities=None + + if self._obs_config.joint_positions: + joint_positions = np.array(arm.get_joint_positions()) + joint_positions = self._obs_config.joint_positions_noise.apply(joint_positions) + else: + joint_positions = None + + if self._obs_config.joint_forces: + fs = arm.get_joint_forces() + vels = arm.get_joint_target_velocities() + joint_forces = np.array([-f if v < 0 else f for f, v in zip(fs, vels)]) + joint_forces = self._obs_config.joint_forces_noise.apply(joint_forces) + else: + joint_forces=None + + if self._obs_config.gripper_open: + if gripper.get_open_amount()[0] > 0.95: + gripper_open = 1.0 + else: + gripper_open = 0.0 + else: + gripper_open = None + + if self._obs_config.gripper_pose: + gripper_pose = tip.get_pose() + else: + gripper_pose = None + + + if self._obs_config.gripper_matrix: + gripper_matrix = tip.get_matrix() + else: + gripper_matrix = None + + if self._obs_config.gripper_touch_forces: + ee_forces = gripper.get_touch_sensor_forces() + ee_forces_flat = [] + for eef in ee_forces: + ee_forces_flat.extend(eef) + gripper_touch_forces = np.array(ee_forces_flat) + else: + gripper_touch_forces = None + + + if self._obs_config.gripper_joint_positions: + gripper_joint_positions= np.array(gripper.get_joint_positions()) + else: + gripper_joint_positions = None + + + if self._obs_config.record_ignore_collisions: + if self._ignore_collisions_for_current_waypoint: + ignore_collisions = np.array(1.0) + else: + ignore_collisions = np.array(0.0) + else: + ignore_collisions = None + + return {"joint_velocities": joint_velocities, + "joint_positions": joint_positions, + "joint_forces": joint_forces, + "gripper_open": gripper_open, + "gripper_pose": gripper_pose, + "gripper_matrix": gripper_matrix, + "gripper_touch_forces": gripper_touch_forces, + "gripper_joint_positions": gripper_joint_positions, + "ignore_collisions": ignore_collisions} + + + if self.robot.is_bimanual: + observation_data["right"] = UnimanualObservationData(**get_proprioception(self.robot.right_arm, self.robot.right_gripper)) + observation_data["left"] = UnimanualObservationData(**get_proprioception(self.robot.left_arm, self.robot.left_gripper)) + else: + observation_data.update(get_proprioception(self.robot.arm, self.robot.gripper)) + + task_low_dim_state=( + self.task.get_low_dim_state() if + self._obs_config.task_low_dim_state else None), + + observation_data.update({ + "task_low_dim_state": task_low_dim_state, + "perception_data": perception_data, + "misc": self._get_misc() + }) + + if self.robot.is_bimanual: + obs = BimanualObservation(**observation_data) + else: + obs = UnimanualObservation(**observation_data) + + obs = self.task.decorate_observation(obs) + + return obs + + def step(self): + self.pyrep.step() + self.task.step() + if self._step_callback is not None: + self._step_callback() + + def register_step_callback(self, func): + self._step_callback = func + + def execute_waypoints_unimanual(self, do_record) -> bool: + waypoints = self.task.get_waypoints() + if len(waypoints) == 0: + raise NoWaypointsError( + 'No waypoints were found.', self.task) + + while True: + success = False + self._ignore_collisions_for_current_waypoint = False + for i, point in enumerate(waypoints): + self._ignore_collisions_for_current_waypoint = point._ignore_collisions + point.start_of_path() + if point.skip: + continue + + colliding_shapes = [] + + grasped_objects = self.robot.gripper.get_grasped_objects() + colliding_shapes = [s for s in self.pyrep.get_objects_in_tree( + object_type=ObjectType.SHAPE) if s not in grasped_objects + and s not in self._robot_shapes and s.is_collidable() + and self.robot.arm.check_arm_collision(s)] + + + logging.info("got list of colliding objects: %s", colliding_shapes) + + [s.set_collidable(False) for s in colliding_shapes] + try: + path = point.get_path() + [s.set_collidable(True) for s in colliding_shapes] + except ConfigurationPathError as e: + logging.error("unable to get path %s", e) + [s.set_collidable(True) for s in colliding_shapes] + raise DemoError( + 'Could not get a path for waypoint %d.' % i, + self.task) from e + ext = point.get_ext() + + logging.info("point.get_ext() %s", str(ext)) + + path.visualize() + + done = False + success = False + while not done: + done = path.step() + self.step() + self._execute_demo_joint_position_action = path.get_executed_joint_position_action() + do_record() + success, term = self.task.success() + + point.end_of_path() + path.clear_visualization() + logging.info("done executing path") + + if len(ext) > 0: + self._handle_extensions_strings(ext, do_record) + + + if not self.task.should_repeat_waypoints() or success: + return success + + + def execute_waypoints_bimanual(self, do_record) -> bool: + right_waypoints = self.task.right_waypoints + left_waypoints = self.task.left_waypoints + + for i, right_point in enumerate(right_waypoints.copy()): + ext = right_point.get_ext() + if 'repeat' in ext: + j = ext.rsplit('_', maxsplit=1) + j = int(j[-1]) + for _ in range(j): + right_waypoints.insert(i, right_point) + + + for i, left_point in enumerate(left_waypoints.copy()): + ext = left_point.get_ext() + if 'repeat' in ext: + j = ext.rsplit('_', maxsplit=1) + j = int(j[-1]) + for _ in range(j): + left_waypoints.insert(i, left_point) + + while len(left_waypoints) > len(right_waypoints): + right_waypoints.append(right_waypoints[-1]) + + while len(right_waypoints) > len(left_waypoints): + left_waypoints.append(left_waypoints[-1]) + + + while True: + success = False + self._ignore_collisions_for_current_waypoint = False + # ..fixme:: some waypoints might be skipped due to zip -> add dummy waypoints + for i, (right_point, left_point) in enumerate(zip(right_waypoints, left_waypoints)): + self._ignore_collisions_for_current_waypoint = right_point._ignore_collisions or left_point._ignore_collisions + right_point.start_of_path() + left_point.start_of_path() + if right_point.skip or left_point.skip: + print("skipping waypoints!") + logging.error("skipping waypoints!") + continue + + grasped_objects = self.robot.right_gripper.get_grasped_objects() + self.robot.left_gripper.get_grasped_objects() + colliding_shapes = [] + for s in self.pyrep.get_objects_in_tree(object_type=ObjectType.SHAPE): + if s in grasped_objects: + continue + #if s in self._robot_shapes: + # continue + if not s.is_collidable(): + continue + if self.robot.right_arm.check_arm_collision(s): + colliding_shapes.append(s) + elif self.robot.left_arm.check_arm_collision(s): + colliding_shapes.append(s) + + logging.debug("got list of colliding objects: %s", ", ".join([s.get_name() for s in colliding_shapes])) + + [s.set_collidable(False) for s in colliding_shapes] + try: + right_path = right_point.get_path() + left_path = left_point.get_path() + except ConfigurationPathError as e: + logging.error("Unable to get path %s", e) + raise DemoError(f'Could not get a path for waypoint {right_point.name} or {left_point.name}.', task=self.task) from e + finally: + [s.set_collidable(True) for s in colliding_shapes] + + right_ext = right_point.get_ext() + left_ext = left_point.get_ext() + + right_path.visualize() + left_path.visualize() + + right_done = False + left_done = False + success = False + while not (right_done and left_done): + if not right_done and right_path.step(): + right_point.end_of_path() + right_path.clear_visualization() + for ext in right_ext.split(";"): + self._handle_extensions_strings(ext.strip(), do_record) + right_done = True + + if not left_done and left_path.step(): + left_point.end_of_path() + left_path.clear_visualization() + for ext in left_ext.split(";"): + self._handle_extensions_strings(ext.strip(), do_record) + left_done = True + + self.step() + self._right_execute_demo_joint_position_action = right_path.get_executed_joint_position_action() + self._left_execute_demo_joint_position_action = left_path.get_executed_joint_position_action() + do_record() + success, term = self.task.success() + + if not self.task.should_repeat_waypoints() or success: + return success + + + def get_demo(self, record: bool = True, + callable_each_step: Callable[[Observation], None] = None, + randomly_place: bool = True) -> Demo: + """Returns a demo (list of observations)""" + + if not self._has_init_task: + self.init_task() + if not self._has_init_episode: + self.init_episode(self._variation_index, + randomly_place=randomly_place) + self._has_init_episode = False + + demo = [] + + def do_record(): + self._demo_record_step(demo, record, callable_each_step) + + if record: + self.pyrep.step() # Need this here or get_force doesn't work... + demo.append(self.get_observation()) + + success = False + if self.robot.is_bimanual: + success = self.execute_waypoints_bimanual(do_record) + else: + success = self.execute_waypoints_unimanual(do_record) + + + # Some tasks may need additional physics steps + # (e.g. ball rowling to goal) + if not success: + for _ in range(10): + self.pyrep.step() + self.task.step() + do_record() + success, term = self.task.success() + if success: + break + + success, term = self.task.success() + if not success: + raise DemoError('Demo was completed, but was not successful.', + self.task) + return Demo(demo) + + def _handle_extensions_strings(self, ext, do_record): + """ + Extensions strings are defined in the field under the 'Common Tab' when editing a waypoint + """ + if len(ext) == 0: + return + + contains_param = False + start_of_bracket = -1 + name = ext.split('_', maxsplit=1)[0] + if 'open_gripper(' in ext: + self.robot.release_gripper(name) + start_of_bracket = ext.index('open_gripper(') + 13 + contains_param = ext[start_of_bracket] != ')' + if not contains_param: + done = False + while not done: + done = self.robot.actutate_gripper(1.0, 0.04, name) + self.pyrep.step() + self.task.step() + if self._obs_config.record_gripper_closing: + do_record() + elif 'close_gripper(' in ext: + start_of_bracket = ext.index('close_gripper(') + 14 + contains_param = ext[start_of_bracket] != ')' + if not contains_param: + done = False + while not done: + done = self.robot.actutate_gripper(0.0, 0.04, name) + self.pyrep.step() + self.task.step() + if self._obs_config.record_gripper_closing: + do_record() + + if contains_param: + rest = ext[start_of_bracket:] + num = float(rest[:rest.index(')')]) + done = False + logging.warning("not tested yet") + while not done: + done = self.robot.actutate_gripper(num, 0.04, name) + self.pyrep.step() + self.task.step() + if self._obs_config.record_gripper_closing: + do_record() + + if 'close_gripper(' in ext: + for g_obj in self.task.get_graspable_objects(): + self.robot.grasp(g_obj, name) + do_record() + + def get_observation_config(self) -> ObservationConfig: + return self._obs_config + + def check_target_in_workspace(self, target_pos: np.ndarray) -> bool: + x, y, z = target_pos + return (self._workspace_maxx > x > self._workspace_minx and + self._workspace_maxy > y > self._workspace_miny and + self._workspace_maxz > z > self._workspace_minz) + + def _demo_record_step(self, demo_list, record, func): + if record: + demo_list.append(self.get_observation()) + if func is not None: + func(self.get_observation()) + + def _set_camera_properties(self) -> None: + def _set_rgb_props(rgb_cam: VisionSensor, + rgb: bool, depth: bool, conf: CameraConfig): + if not (rgb or depth or conf.point_cloud): + rgb_cam.remove() + else: + rgb_cam.set_explicit_handling(1) + rgb_cam.set_resolution(conf.image_size) + rgb_cam.set_render_mode(conf.render_mode) + + def _set_mask_props(mask_cam: VisionSensor, mask: bool, + conf: CameraConfig): + if not mask: + mask_cam.remove() + else: + mask_cam.set_explicit_handling(1) + mask_cam.set_resolution(conf.image_size) + + + for camera_name, camera_config in self._obs_config.camera_configs.items(): + _set_rgb_props(self.camera_sensors[camera_name], camera_config.rgb, camera_config.depth, camera_config) + + if camera_config.mask: + _set_mask_props( + self.camera_sensors_mask[camera_name], + camera_config.mask, + camera_config) + + + def _place_task(self) -> None: + self._workspace_boundary.clear() + # Find a place in the robot workspace for task + self.task.boundary_root().set_orientation( + self._initial_task_pose) + min_rot, max_rot = self.task.base_rotation_bounds() + self._workspace_boundary.sample( + self.task.boundary_root(), + min_rotation=min_rot, max_rotation=max_rot) + + def _get_misc(self): + misc = {} + for camera_name, camera in self.camera_sensors.items(): + if camera.still_exists(): + misc.update({ + f'{camera_name}_camera_extrinsics': camera.get_matrix(), + f'{camera_name}_camera_intrinsics': camera.get_intrinsic_matrix(), + f'{camera_name}_camera_near': camera.get_near_clipping_plane(), + f'{camera_name}_camera_far': camera.get_far_clipping_plane(), + }) + misc.update({"variation_index": self._variation_index}) + if self.robot.is_bimanual and self._right_execute_demo_joint_position_action is not None: + + misc.update({"right_executed_demo_joint_position_action": self._right_execute_demo_joint_position_action, + "left_executed_demo_joint_position_action": self._left_execute_demo_joint_position_action}) + self._right_execute_demo_joint_position_action = None + self._left_execute_demo_joint_position_action = None + + elif not self.robot.is_bimanual and self._execute_demo_joint_position_action is not None: + misc.update({"executed_demo_joint_position_action": self._execute_demo_joint_position_action}) + self._execute_demo_joint_position_action = None + return misc diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/spawn_boundary.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/spawn_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..846981b6e198369f45d021fe289f4033014eeca1 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/spawn_boundary.py @@ -0,0 +1,180 @@ +from typing import List +import numpy as np +import math +from rlbench.backend.exceptions import BoundaryError +from pyrep.objects.object import Object + + +class BoundingBox(object): + def __init__(self, min_x: float, max_x: float, min_y: float, max_y: float, + min_z: float, max_z: float): + self.min_x = min_x + self.max_x = max_x + self.min_y = min_y + self.max_y = max_y + self.min_z = min_z + self.max_z = max_z + self.points = [[min_x, min_y, min_z], [max_x, min_y, min_z], + [min_x, max_y, min_z], [max_x, max_y, min_z], + [min_x, min_y, max_z], [max_x, min_y, max_z], + [min_x, max_y, max_z], [max_x, max_y, max_z]] + + def rotate(self, theta: np.ndarray) -> 'BoundingBox': + r_x = np.array([[1, 0, 0], + [0, math.cos(theta[0]), -math.sin(theta[0])], + [0, math.sin(theta[0]), math.cos(theta[0])] + ]) + r_y = np.array([[math.cos(theta[1]), 0, math.sin(theta[1])], + [0, 1, 0], + [-math.sin(theta[1]), 0, math.cos(theta[1])] + ]) + r_z = np.array([[math.cos(theta[2]), -math.sin(theta[2]), 0], + [math.sin(theta[2]), math.cos(theta[2]), 0], + [0, 0, 1] + ]) + r = np.dot(r_z, np.dot(r_y, r_x)) + new_points = np.dot(self.points, r) + return BoundingBox(np.amin(new_points[:, 0]), np.amax(new_points[:, 0]), + np.amin(new_points[:, 1]), np.amax(new_points[:, 1]), + np.amin(new_points[:, 2]), np.amax(new_points[:, 2])) + + def within_boundary(self, boundary: 'BoundingBox', is_plane: bool) -> bool: + z_cond = True + if not is_plane: + z_cond = self.min_z > boundary.min_z and self.max_z < boundary.max_z + return (self.min_x > boundary.min_x and self.max_x < boundary.max_x and + self.min_y > boundary.min_y and self.max_y < boundary.max_y and + z_cond) + + +class BoundaryObject(object): + + def __init__(self, boundary: Object): + self._boundary = boundary + self._is_plane = False + self._contained_objects = [] + + if boundary.is_model(): + (minx, maxx, miny, + maxy, minz, maxz) = boundary.get_model_bounding_box() + else: + minx, maxx, miny, maxy, minz, maxz = boundary.get_bounding_box() + self._boundary_bbox = BoundingBox(minx, maxx, miny, maxy, minz, maxz) + + height = np.abs(maxz - minz) + if height == 0: + height = 1.0 + self._is_plane = True + self._area = np.abs(maxx - minx) * np.abs(maxy - miny) * height + + def _get_position_within_boundary(self, obj: Object, obj_bbox: BoundingBox + ) -> List[float]: + x = np.random.uniform( + self._boundary_bbox.min_x + np.abs(obj_bbox.min_x), + self._boundary_bbox.max_x - np.abs(obj_bbox.max_x)) + y = np.random.uniform( + self._boundary_bbox.min_y + np.abs(obj_bbox.min_y), + self._boundary_bbox.max_y - np.abs(obj_bbox.max_y)) + if self._is_plane: + _, _, z = obj.get_position(self._boundary) + else: + z = np.random.uniform( + self._boundary_bbox.min_z + np.abs(obj_bbox.min_z), + self._boundary_bbox.max_z - np.abs(obj_bbox.max_z)) + return [x, y, z] + + def get_area(self) -> float: + return self._area + + def add(self, obj: Object, ignore_collisions: bool = False, + min_rotation: tuple = (0.0, 0.0, -3.14), + max_rotation: tuple = (0.0, 0.0, 3.14), + min_distance: float = 0.01) -> int: + """Returns true if can add and adds it + rotation_limits: how mush we allow it to rotate from its original + position""" + + # Rotate the bounding box randomly + if obj.is_model(): + bb = obj.get_model_bounding_box() + else: + bb = obj.get_bounding_box() + obj_bbox = BoundingBox(*bb) + rotation = np.random.uniform(list(min_rotation), list(max_rotation)) + obj_bbox = obj_bbox.rotate(rotation) + + if not obj_bbox.within_boundary(self._boundary_bbox, self._is_plane): + return -1 + + new_pos = self._get_position_within_boundary(obj, obj_bbox) + obj.set_position(new_pos, self._boundary) + obj.rotate(list(rotation)) + new_pos = np.array(new_pos) + + if not ignore_collisions: + for contained_obj in self._contained_objects: + # Check for collision between each child + for cont_ob in contained_obj.get_objects_in_tree( + exclude_base=False): + for placing_ob in obj.get_objects_in_tree( + exclude_base=False): + if placing_ob.check_collision(cont_ob): + return -2 + dist = np.linalg.norm( + new_pos - contained_obj.get_position(self._boundary)) + if dist < min_distance: + return -3 + self._contained_objects.append(obj) + return 1 + + def clear(self) -> None: + self._contained_objects = [] + + +class SpawnBoundary(object): + + MAX_SAMPLES = 100 + + def __init__(self, boundaries: List[Object]): + self._boundaries = [] + areas = [] + for b in boundaries: + bo = BoundaryObject(b) + areas.append(bo.get_area()) + self._boundaries.append(bo) + self._probabilities = np.array(areas) / np.sum(areas) + + def sample(self, obj: Object, ignore_collisions=False, + min_rotation=(0.0, 0.0, -3.14), max_rotation=(0.0, 0.0, 3.14), + min_distance=0.01) -> None: + """ + returns a position within the specified boundary. Calculated using a + weighted percentage. + set ignore_collisions if you are happy with things being stacked on top of each other + set rotation_range to be (0, 0) if you don't want it to rotate + :return: + """ + collision_fails = boundary_fails = self.MAX_SAMPLES + while collision_fails > 0 and boundary_fails > 0: + sampled_boundary = np.random.choice(self._boundaries, + p=self._probabilities) + result = sampled_boundary.add( + obj, ignore_collisions, min_rotation, max_rotation, min_distance) + if result == -1: + boundary_fails -= 1 + elif result == -2: + collision_fails -= 1 + elif result == -3: + boundary_fails -= 1 + else: + break + if boundary_fails <= 0: + raise BoundaryError('Could not place within boundary.' + 'Perhaps the object is too big for it?') + elif collision_fails <= 0: + raise BoundaryError( + 'Could not place the object within the boundary due to ' + 'collision with other objects in the boundary.') + + def clear(self) -> None: + [b.clear() for b in self._boundaries] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/task.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/task.py new file mode 100644 index 0000000000000000000000000000000000000000..29c56ebed6f768b8d657a8bf8f72ff48e5c5f77a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/task.py @@ -0,0 +1,477 @@ +import os +import re +from os.path import dirname, abspath, join +from typing import List, Tuple, Callable, Union + +import numpy as np +from pyrep import PyRep +from pyrep.const import ObjectType +from pyrep.errors import ConfigurationPathError +from pyrep.objects.cartesian_path import CartesianPath +from pyrep.objects.dummy import Dummy +from pyrep.objects.force_sensor import ForceSensor +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.robots.arms.dual_panda import PandaRight +from pyrep.robots.arms.dual_panda import PandaLeft + +from rlbench.backend.conditions import Condition +from rlbench.backend.exceptions import WaypointError +from rlbench.backend.observation import Observation +from rlbench.backend.robot import BimanualRobot, Robot, UnimanualRobot +from rlbench.backend.waypoints import Point, PredefinedPath, Waypoint + +import logging + +TASKS_PATH = join(dirname(abspath(__file__)), '../tasks') +BIMANUAL_TASKS_PATH = join(dirname(abspath(__file__)), '../bimanual_tasks') + + +class Task(object): + + def __init__(self, pyrep: PyRep, robot: Robot, name: str = None): + """Constructor. + + :param pyrep: Instance of PyRep. + :param robot: Instance of Robot. + """ + self.pyrep = pyrep + self.name = name if name else re.sub( + '(? None: + """Initialises the task. Called only once when task loaded. + + Here we can grab references to objects in the task and store them + as member variables to be used in init_episode. Here we also usually + set success conditions for the task as well as register what objects + can be grasped. + """ + raise NotImplementedError( + "'init_task' is almost always necessary.") + + def init_episode(self, index: int) -> List[str]: + """Initialises the episode. Called each time the scene is reset. + + Here we usually define how the task changes across variations. Based on + this we can change the task descriptions that are returned. + + :param index: The variation index. + :return: A list of strings describing the task. + """ + raise NotImplementedError( + "'init_episode' must be defined and return a list of strings.") + + def variation_count(self) -> int: + """Number of variations for the task. Can be determined dynamically. + + :return: Number of variations for this task. + """ + raise NotImplementedError( + "'variation_count' must be defined and return an int.") + + def get_low_dim_state(self) -> np.ndarray: + """Gets the pose and various other properties of objects in the task. + + :return: 1D array of low-dimensional task state. + """ + + # Corner cases: + # (1) Object has been deleted. + # (2) Object has been grasped (and is now child of gripper). + + state = [] + for obj, objtype in self._initial_objs_in_scene: + if not obj.still_exists(): + # It has been deleted + empty_len = 7 + if objtype == ObjectType.JOINT: + empty_len += 1 + elif objtype == ObjectType.FORCE_SENSOR: + empty_len += 6 + state.extend(np.zeros((empty_len,)).tolist()) + else: + state.extend(np.array(obj.get_pose())) + if obj.get_type() == ObjectType.JOINT: + state.extend([Joint(obj.get_handle()).get_joint_position()]) + elif obj.get_type() == ObjectType.FORCE_SENSOR: + forces, torques = ForceSensor(obj.get_handle()).read() + state.extend(forces + torques) + + return np.array(state).flatten() + + def step(self) -> None: + """Called each time the simulation is stepped. Can usually be left.""" + pass + + def reward(self) -> Union[float, None]: + """Allows the user to customise the task and add reward shaping.""" + return None + + def cleanup(self) -> None: + """Called at the end of the episode. Can usually be left. + + Can be used for complex tasks that spawn many objects. + """ + pass + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + """Defines how much the task base can rotate during episode placement. + + Default is set such that it can rotate any amount on the z axis. + + :return: A tuple containing the min and max (x, y, z) rotation bounds + (in radians). + """ + return (0.0, 0.0, -3.14), (0.0, 0.0, 3.14) + + def boundary_root(self) -> Object: + """An object that should act as the task root when randomly placing. + + Sometimes the task can involve a large appliance (e.g. dish washer) + which cannot be placed within the task workspace. This allows us to + define a proxy root (e.g. a plane in front of the appliance) that + would allow the task to be placed inside the workspace. + + :return: The PyRep Object that will act as the root. + """ + return self.get_base() + + def decorate_observation(self, observation: Observation) -> Observation: + """Can be used for tasks that want to modify the observations. + + Usually not used. Perhpas cabn be used to model + + :param observation: The Observation for this time step. + :return: The modified Observation. + """ + return observation + + def is_static_workspace(self) -> bool: + """Specify if the task should'nt be randomly placed in the workspace. + + :return: True if the task pose should not be sampled. + """ + return False + + def set_initial_objects_in_scene(self): + objs = self.get_base().get_objects_in_tree( + exclude_base=True, first_generation_only=False) + types = [ob.get_type() for ob in objs] + self._initial_objs_in_scene = list(zip(objs, types)) + + + ######################### + # Registering functions # + ######################### + + def register_success_conditions(self, condition: List[Condition]): + """What conditions need to be met for the task to be a success. + + Note: this replaces any previously registered conditions! + + :param condition: A list of success conditions. + """ + self._success_conditions = condition + + def register_fail_conditions(self, condition: List[Condition]): + """What conditions need to be met for the task to be classed as a fail. + + Usually this is not needed, however, in some cases, it may be desirable + for tasks to be classed as a fail when certain conditions are met, e.g. + dropping a fragile object, touching something, etc. + Note: this replaces any previously registered conditions! + + :param condition: A list of fail conditions. + """ + self._fail_conditions = condition + + def register_graspable_objects(self, objects: List[Object]): + """Register what objects can be grasped with a 'stable' grasp. + + In order for objects to be grasped in a stable way, PyRep attaches an + objects that need to be grasped as a child of the gripper. This function + allows one to register a list of objects that can be grasped in + this 'stable' manner. + + Note: this replaces any previously registered objects! + + :param objects: The list of Objects that can be grasped. + """ + self._graspable_objects = objects + + def register_waypoint_ability_start(self, waypoint_index: int, + func: Callable[[Waypoint], None]): + """Register a function to be called before moving to waypoint. + + The registered function should take in a Waypoint object and is called + when the robot is about to move to the assigned waypoint. + + + :param waypoint_index: The waypoint index. + :param func: A function that takes a Waypoint object. + """ + self._waypoint_abilities_start[waypoint_index] = func + + def register_waypoint_ability_end(self, waypoint_index: int, + func: Callable[[Waypoint], None]): + """Register a function to be called after moving to waypoint. + + The registered function should take in a Waypoint object and is called + when the robot has finished moving to the assigned waypoint. + + + :param waypoint_index: The waypoint index. + :param func: A function that takes a Waypoint object. + """ + self._waypoint_abilities_end[waypoint_index] = func + + def register_waypoints_should_repeat(self, func: Callable[[], bool]): + """Register a function that is called when reached the end of a demo. + + The registered function should return a bool if the demo should repeat. + Can be used in conjunction with `register_waypoint_ability_start` and + `register_waypoint_ability_end`. Useful for a task such as emptying a + container, where we want to keep dynamically moving waypoints until + the container is empty. + + :param func: A function that return a bool if the demo should repeat. + """ + self._waypoints_should_repeat = func + + def register_stop_at_waypoint(self, waypoint_index: int): + """Register at what index the demo should be stopped. + + :param waypoint_index: The waypoint index. + """ + self._stop_at_waypoint_index = waypoint_index + + ########################## + # Other public functions # + ########################## + + def get_name(self) -> str: + """The name of the task file (without the .py extension). + + :return: The name of the task. + """ + return self.name + + def validate(self): + """If the task placement is valid. """ + self._waypoints = self._get_waypoints() + + def get_waypoints(self): + if self._waypoints is None: + self._waypoints = self._get_waypoints() + return self._waypoints + + def should_repeat_waypoints(self): + return self._waypoints_should_repeat() + + def get_graspable_objects(self): + return self._graspable_objects + + def success(self) -> Tuple[bool, bool]: + """If the task is currently successful. + + :return: Tuple containing 2 bools: first specifies if the task is currently successful, + second specifies if the task should terminate (either from success or from broken constraints). + """ + all_fails_met = np.all( + [cond.condition_met()[0] for cond in self._fail_conditions]) + if len(self._fail_conditions) > 0 and all_fails_met: + return False, True + all_met = np.all( + [cond.condition_met()[0] for cond in self._success_conditions]) + should_terminate = all_met + return all_met, should_terminate + + def load(self) -> Object: + if Object.exists(self.name): + return Dummy(self.name) + ttm_file = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + '../task_ttms/%s.ttm' % self.name) + if not os.path.isfile(ttm_file): + raise FileNotFoundError( + 'The following is not a valid task .ttm file: %s' % ttm_file) + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def unload(self) -> None: + self._waypoints = None + self.get_base().remove() + self.clear_registerings() + + def cleanup_(self) -> None: + for cond in self._success_conditions + self._fail_conditions: + cond.reset() + + self._waypoints = None + self.cleanup() + + def clear_registerings(self) -> None: + self._success_conditions = [] + self._fail_conditions = [] + self._graspable_objects = [] + self._base_object = None + self._waypoint_additional_inits = {} + self._waypoint_abilities_start = {} + self._waypoint_abilities_end = {} + + def get_base(self) -> Dummy: + self._base_object = Dummy(self.name) + return self._base_object + + def get_state(self) -> Tuple[bytes, int]: + objs = self.get_base().get_objects_in_tree(exclude_base=False) + return self.get_base().get_configuration_tree(), len(objs) + + def restore_state(self, state: Tuple[bytes, int]) -> None: + objs = self.get_base().get_objects_in_tree(exclude_base=False) + if len(objs) != state[1]: + if self.name not in ['empty_container']: + logging.error('Expected to be resetting %d objects, but there were %d.', state[1], len(objs)) + raise RuntimeError("Insufficient number of objects in the scene. Maybe an object is still grasped. Please check the task!") + self.pyrep.set_configuration_tree(state[0]) + + ##################### + # Private functions # + ##################### + def _feasible(self, waypoints: List[Point]) -> Tuple[bool, int]: + if isinstance(self.robot, UnimanualRobot): + arm = self.robot.arm + logging.warn("single robot") + return self._feasible_with_arm(arm, waypoints) + elif isinstance(self.robot, BimanualRobot): + way_points_right = [w for w in waypoints if isinstance(w._arm, PandaRight)] + way_points_left = [w for w in waypoints if isinstance(w._arm, PandaLeft)] + for point in way_points_right: + waypoint = point._waypoint + arm = point._arm + logging.info("total waypoints %s, (right=%s, left=%s)", len(waypoints), len(way_points_right), len(way_points_left)) + + f_right = self._feasible_with_arm(self.robot.right_arm, way_points_right) + f_left = self._feasible_with_arm(self.robot.left_arm, way_points_left) + if f_right[0] and f_left[0]: + return True, -1 + else: + logging.warn("Waypoints are not reachable right=%s left=%s", f_right, f_left) + return False, (f_right, f_left) + + else: + logging.error('Invalid robot') + + def _feasible_with_arm(self, arm, waypoints: List[Point]) -> Tuple[bool, str]: + start_vals = arm.get_joint_positions() + for i, point in enumerate(waypoints): + path = None + try: + path = point.get_path(ignore_collisions=True) + except ConfigurationPathError as err: + pass + if path is None: + arm.set_joint_positions(start_vals) + + return False, str(point) + path.set_to_end() + # Needed twice otherwise can glitch out. + arm.set_joint_positions(start_vals) + return True, "" + + def _get_waypoints(self, validating=False) -> List[Waypoint]: + waypoint_name = 'waypoint%d' + waypoints = [] + additional_waypoint_inits = [] + i = 0 + while True: + name = waypoint_name % i + if i == self._stop_at_waypoint_index or not Object.exists(name): + # There are no more waypoints... + break + ob_type = Object.get_object_type(name) + way = None + if ob_type == ObjectType.DUMMY: + waypoint = Dummy(name) + start_func = None + end_func = None + if i in self._waypoint_abilities_start: + start_func = self._waypoint_abilities_start[i] + if i in self._waypoint_abilities_end: + end_func = self._waypoint_abilities_end[i] + if isinstance(self.robot, UnimanualRobot): + arm = self.robot.arm + way = Point(waypoint, arm, + start_of_path_func=start_func, + end_of_path_func=end_func) + elif isinstance(self.robot, BimanualRobot): + waypoint_mapping = self.waypoint_mapping[name] + logging.debug("mapping waypoint %s to %s", name, waypoint_mapping) + arms = self.robot.get_arms_by_name(waypoint_mapping) + if not arms: + logging.warning("unable to get arm for waypoint %s", name) + for arm in arms: + way = Point(waypoint, arm, + start_of_path_func=start_func, + end_of_path_func=end_func) + elif ob_type == ObjectType.PATH: + cartestian_path = CartesianPath(name) + way = PredefinedPath(cartestian_path, arm) + else: + raise WaypointError( + '%s is an unsupported waypoint type %s' % ( + name, ob_type), self) + + if name in self._waypoint_additional_inits and not validating: + additional_waypoint_inits.append( + (self._waypoint_additional_inits[name], way)) + waypoints.append(way) + i += 1 + + # Check if all of the waypoints are feasible + feasible, way_i = self._feasible(waypoints) + if not feasible: + raise WaypointError( + "Infeasible episode. Can't reach waypoint %s." % str(way_i), self) + for func, way in additional_waypoint_inits: + func(way) + return waypoints + + + +class BimanualTask(Task): + + def __init__(self, pyrep: PyRep, robot: Robot, name: str = None): + if not isinstance(robot, BimanualRobot): + logging.error("tasks requires a bimanual robot") + super().__init__(pyrep, robot, name) + + @property + def right_waypoints(self): + waypoints = self.get_waypoints() + wm = self.waypoint_mapping + return [w for w in waypoints if wm[w.name] == 'right'] + + @property + def left_waypoints(self): + waypoints = self.get_waypoints() + wm = self.waypoint_mapping + return [w for w in waypoints if wm[w.name] == 'left'] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/task_utils.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/task_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f7bfc6329932e2d27cb9a9a591f7e857c725497d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/task_utils.py @@ -0,0 +1,28 @@ +import os +import numpy as np +from pyrep.objects.shape import Shape + + +def sample_procedural_objects(task_base, num_samples, mass=0.1): + assets_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), + '../assets/procedural_objects') + samples = np.random.choice( + os.listdir(assets_dir), num_samples, replace=False) + created = [] + for s in samples: + respondable = os.path.join(assets_dir, s, s + '_coll.obj') + visual = os.path.join(assets_dir, s, s + '.obj') + resp = Shape.import_mesh(respondable, scaling_factor=0.005) + vis = Shape.import_mesh(visual, scaling_factor=0.005) + resp.set_renderable(False) + vis.set_renderable(True) + vis.set_parent(resp) + vis.set_dynamic(False) + vis.set_respondable(False) + resp.set_dynamic(True) + resp.set_mass(mass) + resp.set_respondable(True) + resp.set_model(True) + resp.set_parent(task_base) + created.append(resp) + return created diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/utils.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9cd1f492e2537f8020c941e41dbe0735238db905 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/utils.py @@ -0,0 +1,231 @@ +"""Creates an image from a numpy array of floating point depth data. + +Taken from https://github.com/ahundt/robotics_setup/blob/master/datasets/google_brain_robot_data/depth_image_encoding.py + +Examples: + + depth_array is a 2D numpy array of floating point depth data in meters. + + depth_rgb = FloatArrayToRgbImage(depth_array) + depth_rgb is a PIL Image object containing the same data as 24-bit + integers encoded in the RGB bytes. + depth_rgb.save('image_file.png') - to save to a file. + + depth_array2 = ImageToFloatArray(depth_rgb) + depth_array2 is a 2D numpy array containing the same data as + depth_array up to the precision of the RGB image (1/256 mm). + + depth_gray = FloatArrayToGrayImage(depth_array) + depth_gray is a PIL Image object containing the same data rounded to + 8-bit integers. + depth_gray.save('image_file.jpg', quality=95) - to save to a file. + + depth_array3 = ImageToFloatArray(depth_gray) + depth_array3 is a 2D numpy array containing the same data as + depth_array up to the precision of the grayscale image (1 cm). + +The image conversions first scale and round the values and then pack +them into the desired type in a numpy array before converting the +array to a PIL Image object. The Image can be saved in any format. +We are using PNG for RGB and high quality JPEG for grayscale images. + +You can use different numeric types (e.g. np.uint16, np.int32), but +not all combinations of numeric type and image format are supported by +PIL or standard image viewers. + +""" + +import numpy as np +from PIL import Image + + +def ClipFloatValues(float_array, min_value, max_value): + """Clips values to the range [min_value, max_value]. + + First checks if any values are out of range and prints a message. + Then clips all values to the given range. + + Args: + float_array: 2D array of floating point values to be clipped. + min_value: Minimum value of clip range. + max_value: Maximum value of clip range. + + Returns: + The clipped array. + + """ + if float_array.min() < min_value or float_array.max() > max_value: + float_array = np.clip(float_array, min_value, max_value) + return float_array + + +DEFAULT_RGB_SCALE_FACTOR = 256000.0 + + +def float_array_to_rgb_image(float_array, + scale_factor=DEFAULT_RGB_SCALE_FACTOR, + drop_blue=False): + """Convert a floating point array of values to an RGB image. + + Convert floating point values to a fixed point representation where + the RGB bytes represent a 24-bit integer. + R is the high order byte. + B is the low order byte. + The precision of the depth image is 1/256 mm. + + Floating point values are scaled so that the integer values cover + the representable range of depths. + + This image representation should only use lossless compression. + + Args: + float_array: Input array of floating point depth values in meters. + scale_factor: Scale value applied to all float values. + drop_blue: Zero out the blue channel to improve compression, results in 1mm + precision depth values. + + Returns: + 24-bit RGB PIL Image object representing depth values. + """ + # Scale the floating point array. + scaled_array = np.floor(float_array * scale_factor + 0.5) + # Convert the array to integer type and clip to representable range. + min_inttype = 0 + max_inttype = 2**24 - 1 + scaled_array = ClipFloatValues(scaled_array, min_inttype, max_inttype) + int_array = scaled_array.astype(np.uint32) + # Calculate: + # r = (f / 256) / 256 high byte + # g = (f / 256) % 255 middle byte + # b = f % 255 low byte + rg = np.divide(int_array, 256) + r = np.divide(rg, 256) + g = np.mod(rg, 255) + image_shape = int_array.shape + rgb_array = np.zeros((image_shape[0], image_shape[1], 3), dtype=np.uint8) + rgb_array[..., 0] = r + rgb_array[..., 1] = g + if not drop_blue: + # Calculate the blue channel and add it to the array. + b = np.mod(int_array, 255) + rgb_array[..., 2] = b + image_mode = 'RGB' + image = Image.fromarray(rgb_array, mode=image_mode) + return image + + +DEFAULT_GRAY_SCALE_FACTOR = {np.uint8: 100.0, + np.uint16: 1000.0, + np.int32: DEFAULT_RGB_SCALE_FACTOR} + + +def float_array_to_grayscale_image(float_array, scale_factor=None, image_dtype=np.uint8): + """Convert a floating point array of values to an RGB image. + + Convert floating point values to a fixed point representation with + the given bit depth. + + The precision of the depth image with default scale_factor is: + uint8: 1cm, with a range of [0, 2.55m] + uint16: 1mm, with a range of [0, 65.5m] + int32: 1/256mm, with a range of [0, 8388m] + + Right now, PIL turns uint16 images into a very strange format and + does not decode int32 images properly. Only uint8 works correctly. + + Args: + float_array: Input array of floating point depth values in meters. + scale_factor: Scale value applied to all float values. + image_dtype: Image datatype, which controls the bit depth of the grayscale + image. + + Returns: + Grayscale PIL Image object representing depth values. + + """ + # Ensure that we have a valid numeric type for the image. + if image_dtype == np.uint16: + image_mode = 'I;16' + elif image_dtype == np.int32: + image_mode = 'I' + else: + image_dtype = np.uint8 + image_mode = 'L' + if scale_factor is None: + scale_factor = DEFAULT_GRAY_SCALE_FACTOR[image_dtype] + # Scale the floating point array. + scaled_array = np.floor(float_array * scale_factor + 0.5) + # Convert the array to integer type and clip to representable range. + min_dtype = np.iinfo(image_dtype).min + max_dtype = np.iinfo(image_dtype).max + scaled_array = ClipFloatValues(scaled_array, min_dtype, max_dtype) + + image_array = scaled_array.astype(image_dtype) + image = Image.fromarray(image_array, mode=image_mode) + return image + + +def image_to_float_array(image, scale_factor=None): + """Recovers the depth values from an image. + + Reverses the depth to image conversion performed by FloatArrayToRgbImage or + FloatArrayToGrayImage. + + The image is treated as an array of fixed point depth values. Each + value is converted to float and scaled by the inverse of the factor + that was used to generate the Image object from depth values. If + scale_factor is specified, it should be the same value that was + specified in the original conversion. + + The result of this function should be equal to the original input + within the precision of the conversion. + + Args: + image: Depth image output of FloatArrayTo[Format]Image. + scale_factor: Fixed point scale factor. + + Returns: + A 2D floating point numpy array representing a depth image. + + """ + image_array = np.array(image) + image_dtype = image_array.dtype + image_shape = image_array.shape + + channels = image_shape[2] if len(image_shape) > 2 else 1 + assert 2 <= len(image_shape) <= 3 + if channels == 3: + # RGB image needs to be converted to 24 bit integer. + float_array = np.sum(image_array * [65536, 256, 1], axis=2) + if scale_factor is None: + scale_factor = DEFAULT_RGB_SCALE_FACTOR + else: + if scale_factor is None: + scale_factor = DEFAULT_GRAY_SCALE_FACTOR[image_dtype.type] + float_array = image_array.astype(np.float32) + scaled_array = float_array / scale_factor + return scaled_array + + +def task_file_to_task_class(task_file, bimanual=False): + #..fixme:: duplicate rlbench.utils.name_to_task_class + import importlib + name = task_file.replace('.py', '') + class_name = ''.join([w[0].upper() + w[1:] for w in name.split('_')]) + if bimanual: + mod = importlib.import_module("rlbench.bimanual_tasks.%s" % name) + else: + mod = importlib.import_module("rlbench.tasks.%s" % name) + mod = importlib.reload(mod) + task_class = getattr(mod, class_name) + return task_class + + +def rgb_handles_to_mask(rgb_coded_handles): + # rgb_coded_handles should be (w, h, c) + # Handle encoded as : handle = R + G * 256 + B * 256 * 256 + rgb_coded_handles *= 255 # takes rgb range to 0 -> 255 + rgb_coded_handles.astype(int) + return (rgb_coded_handles[:, :, 0] + + rgb_coded_handles[:, :, 1] * 256 + + rgb_coded_handles[:, :, 2] * 256 * 256) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/waypoints.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/waypoints.py new file mode 100644 index 0000000000000000000000000000000000000000..384dc76c41a269873104626557520ccf9ccdf36f --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/backend/waypoints.py @@ -0,0 +1,83 @@ +from pyrep.const import ConfigurationPathAlgorithms as Algos +from pyrep.objects.object import Object +from pyrep.robots.configuration_paths.arm_configuration_path import ( + ArmConfigurationPath) + +from pyrep.robots.arms.arm import Arm + +import logging + + + +class Waypoint(object): + + def __init__(self, waypoint: Object, arm: Arm, ignore_collisions=False, + start_of_path_func=None, end_of_path_func=None): + self._waypoint = waypoint + self._arm = arm + self._ext = waypoint.get_extension_string() + self._ignore_collisions = ignore_collisions + self._linear_only = False + self._start_of_path_func = start_of_path_func + self._end_of_path_func = end_of_path_func + self.skip = False + if len(self._ext) > 0: + self._ignore_collisions = 'ignore_collision' in self._ext + self._linear_only = 'linear' in self._ext + + def get_path(self, ignore_collisions=False) -> ArmConfigurationPath: + raise NotImplementedError() + + def get_ext(self) -> str: + return self._ext + + def get_waypoint_object(self) -> Object: + return self._waypoint + + def remove(self) -> None: + self._waypoint.remove() + + def start_of_path(self) -> None: + if self._start_of_path_func is not None: + self._start_of_path_func(self) + + def end_of_path(self) -> None: + if self._end_of_path_func is not None: + self._end_of_path_func(self) + + @property + def name(self) -> str: + return self._waypoint.get_object_name(self._waypoint._handle) + + def __str__(self): + waypoint_name = self._waypoint.get_object_name(self._waypoint._handle) + arm_name = self._arm.get_object_name(self._arm._handle) + return f"{waypoint_name} - {arm_name}" + + + +class Point(Waypoint): + + def get_path(self, ignore_collisions=False) -> ArmConfigurationPath: + if self._linear_only: + path = self._arm.get_linear_path(self._waypoint.get_position(), + euler=self._waypoint.get_orientation(), + ignore_collisions=(self._ignore_collisions or + ignore_collisions)) + else: + path = self._arm.get_path(self._waypoint.get_position(), + euler=self._waypoint.get_orientation(), + ignore_collisions=(self._ignore_collisions or + ignore_collisions), + trials=100, + max_configs=10, + trials_per_goal=10, + algorithm=Algos.RRTConnect) + return path + + +class PredefinedPath(Waypoint): + + def get_path(self, ignore_collisions=False) -> ArmConfigurationPath: + path = self._arm.get_path_from_cartesian_path(self._waypoint) + return path diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/__init__.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_pick_laptop.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_pick_laptop.py new file mode 100644 index 0000000000000000000000000000000000000000..a1d5067e8016eb17ce187fc91db843bf037657a6 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_pick_laptop.py @@ -0,0 +1,35 @@ +from typing import List +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import Condition + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + + + +class BimanualPickLaptop(BimanualTask): + + def init_task(self) -> None: + self.register_success_conditions([LiftedCondition(Shape('lid'), 1.0)]) + self.waypoint_mapping = defaultdict(lambda: 'left') + for i in range(1, 7, 2): + self.waypoint_mapping.update({f'waypoint{i}': 'right'}) + + def init_episode(self, index: int) -> List[str]: + return ['pick up the laptop'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_pick_plate.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_pick_plate.py new file mode 100644 index 0000000000000000000000000000000000000000..375dae2d3ffc4e208fae04b55a0a718abb910dbe --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_pick_plate.py @@ -0,0 +1,40 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import BimanualTask +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from collections import defaultdict + + +from rlbench.backend.conditions import Condition + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + + + +class BimanualPickPlate(BimanualTask): + + def init_task(self) -> None: + self.plate = Shape('plate') + + self.register_success_conditions([ LiftedCondition(self.plate, 1.0)]) + self.register_graspable_objects([self.plate]) + self.waypoint_mapping = defaultdict(lambda: 'right') + self.waypoint_mapping.update({'waypoint0': 'left', 'waypoint2': 'left', 'waypoint6': 'left'}) + + def init_episode(self, index: int) -> List[str]: + return ['pick up the plate'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, 0.0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_push_single_button.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_push_single_button.py new file mode 100644 index 0000000000000000000000000000000000000000..ad7e91aa9e096a4da825a58ba19a766641dccd11 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_push_single_button.py @@ -0,0 +1,176 @@ +from typing import List +import itertools +import math +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import JointCondition, ConditionSet +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +MAX_TARGET_BUTTONS = 1 +MAX_VARIATIONS = 50 + +# button top plate and wrapper will be be red before task completion +# and be changed to cyan upon success of task, so colors list used to randomly vary colors of +# base block will be redefined, excluding red and green +colors = [ + ('maroon', (0.5, 0.0, 0.0)), + ('green', (0.0, 0.5, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('navy', (0.0, 0.0, 0.5)), + ('yellow', (1.0, 1.0, 0.0)), + ('cyan', (0.0, 1.0, 1.0)), + ('magenta', (1.0, 0.0, 1.0)), + ('silver', (0.75, 0.75, 0.75)), + ('gray', (0.5, 0.5, 0.5)), + ('orange', (1.0, 0.5, 0.0)), + ('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + ('teal', (0, 0.5, 0.5)), + ('azure', (0.0, 0.5, 1.0)), + ('violet', (0.5, 0.0, 1.0)), + ('rose', (1.0, 0.0, 0.5)), + ('black', (0.0, 0.0, 0.0)), + ('white', (1.0, 1.0, 1.0)), +] + +color_permutations = list(itertools.permutations(colors, 3)) + + +class BimanualPushSingleButton(BimanualTask): + + + def init_task(self) -> None: + self.buttons_pushed = 0 + self.color_variation_index = 0 + self.target_buttons = [Shape('push_buttons_target%d' % i) + for i in range(3)] + self.target_topPlates = [Shape('target_button_topPlate%d' % i) + for i in range(3)] + self.target_joints = [Joint('target_button_joint%d' % i) + for i in range(3)] + self.target_wraps = [Shape('target_button_wrap%d' % i) + for i in range(3)] + self.boundaries = Shape('push_buttons_boundary') + # goal_conditions merely state joint conditions for push action for + # each button regardless of whether the task involves pushing it + self.goal_conditions = [JointCondition(self.target_joints[n], 0.001) + for n in range(3)] + + self.waypoint_mapping = defaultdict(lambda: 'right') + self.waypoint_mapping.update({'waypoint2': 'left'}) + + #left_tcp = Dummy('Panda_leftArm_tip') +# + #Dummy('waypoint2').set_position(position=(0,0,0), relative_to=left_tcp) + #Dummy('waypoint2').set_orientation(orientation=(0,0,0), relative_to=left_tcp) + + #self.register_waypoint_ability_start(0, self._move_above_next_target) + #self.register_waypoints_should_repeat(self._repeat) + + + def init_episode(self, index: int) -> List[str]: + + + for tp in self.target_topPlates: + tp.set_color([1.0, 0.0, 0.0]) + for w in self.target_wraps: + w.set_color([1.0, 0.0, 0.0]) + # For each color permutation, we want to have 1, 2 or 3 buttons pushed + color_index = int(index / MAX_TARGET_BUTTONS) + self.buttons_to_push = 1 + index % MAX_TARGET_BUTTONS + button_colors = color_permutations[color_index] + + self.color_names = [] + self.color_rgbs = [] + self.chosen_colors = [] + i = 0 + for b in self.target_buttons: + color_name, color_rgb = button_colors[i] + self.color_names.append(color_name) + self.color_rgbs.append(color_rgb) + self.chosen_colors.append((color_name, color_rgb)) + b.set_color(color_rgb) + i += 1 + + # for task success, all button to push must have green color RGB + self.success_conditions = [] + for i in range(self.buttons_to_push): + self.success_conditions.append(self.goal_conditions[i]) + + self.register_success_conditions( + [ConditionSet(self.success_conditions, True, False)]) + + rtn0 = 'push the %s button' % self.color_names[0] + rtn1 = 'press the %s button' % self.color_names[0] + rtn2 = 'push down the button with the %s base' % self.color_names[0] + for i in range(self.buttons_to_push): + if i == 0: + continue + else: + rtn0 += ', then push the %s button' % self.color_names[i] + rtn1 += ', then press the %s button' % self.color_names[i] + rtn2 += ', then the %s one' % self.color_names[i] + + b = SpawnBoundary([self.boundaries]) + for button in self.target_buttons: + b.sample(button, min_distance=0.1) + + num_non_targets = 3 - self.buttons_to_push + spare_colors = list(set(colors) + - set( + [self.chosen_colors[i] for i in range(self.buttons_to_push)])) + + spare_color_rgbs = [] + for i in range(len(spare_colors)): + _, rgb = spare_colors[i] + spare_color_rgbs.append(rgb) + + color_choice_indexes = np.random.choice(range(len(spare_colors)), + size=num_non_targets, + replace=False) + non_target_index = 0 + for i, button in enumerate(self.target_buttons): + if i in range(self.buttons_to_push): + pass + else: + _, rgb = spare_colors[color_choice_indexes[non_target_index]] + button.set_color(rgb) + non_target_index += 1 + + + left_tcp = Dummy('Panda_leftArm_tip') + Dummy('waypoint2').set_position(position=(0,0,0), relative_to=left_tcp) + Dummy('waypoint2').set_orientation(orientation=(0,0,0), relative_to=left_tcp) + + + + w0 = Dummy('waypoint0') + x, y, z = self.target_buttons[0].get_position() + w0.set_position([x, y, z + 0.083]) + w0.set_orientation([math.pi, 0, math.pi]) + + + + return [rtn0, rtn1, rtn2] + + def variation_count(self) -> int: + return np.minimum( + len(color_permutations) * MAX_TARGET_BUTTONS, MAX_VARIATIONS) + + def step(self) -> None: + for i in range(len(self.target_buttons)): + if self.goal_conditions[i].condition_met() == (True, True): + self.target_topPlates[i].set_color([0.0, 1.0, 0.0]) + self.target_wraps[i].set_color([0.0, 1.0, 0.0]) + + def cleanup(self) -> None: + self.buttons_pushed = 0 + + + def is_static_workspace(self): + return True \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_set_the_table.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_set_the_table.py new file mode 100644 index 0000000000000000000000000000000000000000..00f4096dde26785dbea37609957f34cc7a94b635 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_set_the_table.py @@ -0,0 +1,50 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +class BimanualSetTheTable(Task): + + def init_task(self) -> None: + plate = Shape('plate') + fork = Shape('fork') + knife = Shape('knife') + spoon = Shape('spoon') + glass = Shape('glass') + self.register_success_conditions([ + DetectedCondition(plate, ProximitySensor('plate_detector')), + DetectedCondition(fork, ProximitySensor('fork_detector')), + DetectedCondition(knife, ProximitySensor('knife_detector')), + DetectedCondition(spoon, ProximitySensor('spoon_detector')), + DetectedCondition(glass, ProximitySensor('glass_detector')), + NothingGrasped(self.robot.right_gripper), NothingGrasped(self.robot.left_gripper)]) + self.register_graspable_objects([plate, fork, knife, spoon, glass]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + + for i in range(5): + self.waypoint_mapping[f'waypoint{i}'] = 'right' + for i in range(23, 28): + self.waypoint_mapping[f'waypoint{i}'] = 'right' + + def init_episode(self, index: int) -> List[str]: + return ['set the table' + 'place the dishes and cutlery on the table in preparation for ' + 'a meal', + 'pick up the plate and put it down on the table, then place ' + 'the fork to its left, the knife and then the spoon to its ' + 'right, and set the glass down just above them', + 'prepare the table for a meal', + 'arrange the plate, cutlery and glass neatly on the table ' + 'so that a person can eat', + 'get the table ready for lunch', + 'get the table ready for dinner'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, 0.0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_straighten_rope.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_straighten_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..eac1e1f2fedb5f23b20b15c9bdf4165ff6918240 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_straighten_rope.py @@ -0,0 +1,30 @@ +from typing import List +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +class BimanualStraightenRope(BimanualTask): + + def init_task(self) -> None: + self.register_success_conditions( + [DetectedCondition(Shape('head'), ProximitySensor('success_head')), + DetectedCondition(Shape('tail'), ProximitySensor('success_tail'))]) + + self.waypoint_mapping = defaultdict(lambda: 'right') + for i in range(3): + self.waypoint_mapping[f'waypoint{i}'] = 'left' + + def init_episode(self, index: int) -> List[str]: + return ['straighten rope', + 'pull the rope straight', + 'grasping each end of the rope in turn, leave the rope straight' + ' on the table', + 'pull each end of the rope until is is straight', + 'tighten the rope', + 'pull the rope tight'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_sweep_to_dustpan.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_sweep_to_dustpan.py new file mode 100644 index 0000000000000000000000000000000000000000..1c00e7c8acb4c77c5be32d813efcdda70f12619c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/bimanual_sweep_to_dustpan.py @@ -0,0 +1,45 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import BimanualTask +from collections import defaultdict +from rlbench.backend.spawn_boundary import SpawnBoundary + + +DIRT_NUM = 5 + + +class BimanualSweepToDustpan(BimanualTask): + + def init_task(self) -> None: + success_sensor = ProximitySensor('success') + self.dirts = [Shape('dirt' + str(i)) for i in range(DIRT_NUM)] + conditions = [DetectedCondition(dirt, success_sensor) for dirt in self.dirts] + self.register_graspable_objects([Shape('broom'), Shape('Dustpan_4'), Shape('Dustpan_5'), Shape('Dustpan_3')]) + self.register_success_conditions(conditions) + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint5': 'right', 'waypoint6': 'right', 'waypoint7': 'right'}) + + def init_episode(self, index: int) -> List[str]: + + b = SpawnBoundary([Shape('dirt_boundary')]) + b.clear() + for item in self.dirts: + b.sample(item, min_distance=0.01, ignore_collisions=True) + + return ['sweep dirt to dustpan', + 'sweep the dirt up', + 'use the broom to brush the dirt into the dustpan', + 'clean up the dirt', + 'pick up the brush and clean up the table', + 'grasping the broom by its handle, clear way the dirt from the ' + 'table', + 'leave the table clean'] + + def variation_count(self) -> int: + return 1 + + def is_static_workspace(self): + return True diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_close_jar.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_close_jar.py new file mode 100644 index 0000000000000000000000000000000000000000..09067878506be7402b9fa8f04a1bf1361defe259 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_close_jar.py @@ -0,0 +1,86 @@ +from typing import List, Tuple +from rlbench.backend.task import Task +from typing import List +from rlbench.backend.task import Task +from rlbench.const import colors +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.robot import BimanualRobot +from rlbench.backend.robot import Robot +from rlbench.backend.task import BimanualTask + +from pyrep.objects.dummy import Dummy +from pyrep import PyRep +from collections import defaultdict + +class CoordinatedCloseJar(BimanualTask): + + # waypoints for right robot: 0,1,2 + # place / pickup cup 6,7,8 + + def init_task(self) -> None: + self.lid = Shape('jar_lid0') + self.jars = [Shape('jar%d' % i) for i in range(2)] + self.register_graspable_objects([self.lid]) + self.boundary = Shape('spawn_boundary') + self.conditions = [NothingGrasped(self.robot.left_gripper), NothingGrasped(self.robot.right_gripper)] + + self.waypoint_mapping = defaultdict(lambda: 'left') + for i in range(7): + self.waypoint_mapping[f'waypoint{i}'] = 'right' + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + b = SpawnBoundary([self.boundary]) + for obj in self.jars: + print(f"print sampling for jar {obj}") + b.sample(obj, min_distance=0.01) + print("ok") + success = ProximitySensor('success') + success.set_position([0.0, 0.0, 0.05], relative_to=self.jars[index % 2], + reset_dynamics=False) + + w0 = Dummy('waypoint0') + w0.set_orientation([-np.pi, 0, -np.pi], reset_dynamics=False) + w0.set_position([0.0, 0.0, 0.125], relative_to=self.lid, + reset_dynamics=False) + + + w3 = Dummy('waypoint10') + w3.set_orientation([-np.pi, 0, -np.pi], reset_dynamics=False) + w3.set_position([0.0, 0.0, 0.125], relative_to=self.jars[index % 2], + reset_dynamics=False) + target_color_name, target_color_rgb = colors[index] + color_choice = np.random.choice( + list(range(index)) + list( + range(index + 1, len(colors))), + size=1, replace=False)[0] + _, distractor_color_rgb = colors[color_choice] + self.jars[index % 2].set_color(target_color_rgb) + other_index = {0: 1, 1: 0} + self.jars[other_index[index % 2]].set_color(distractor_color_rgb) + self.conditions += [DetectedCondition(self.lid, success)] + self.register_success_conditions(self.conditions) + return ['close the %s jar' % target_color_name, + 'screw on the %s jar lid' % target_color_name, + 'grasping the lid, lift it from the table and use it to seal ' + 'the %s jar' % target_color_name, + 'pick up the lid from the table and put it on the %s jar' + % target_color_name] + + def variation_count(self) -> int: + return len(colors) + + def cleanup(self) -> None: + self.conditions = [NothingGrasped(self.robot.left_gripper), NothingGrasped(self.robot.right_gripper)] + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + # This is here to stop the issue of gripper rotation joint reaching its + # limit and not being able to go through the full range of rotation to + # unscrew, leading to a weird jitery and tilted cap while unscrewing. + # Issue occured rarely so is only minor + return (0.0, 0.0, -0.6*np.pi), (0.0, 0.0, +0.6*np.pi) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_ball.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_ball.py new file mode 100644 index 0000000000000000000000000000000000000000..1d38238ad1d6e418da2de35707b81c4cb558e893 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_ball.py @@ -0,0 +1,50 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from pyrep.objects.object import Object +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.conditions import Condition +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.spawn_boundary import SpawnBoundary + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + + +class CoordinatedLiftBall(BimanualTask): + + def init_task(self) -> None: + self.ball = Shape('ball') + #self.register_graspable_objects([self.tray]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + for i in range(0, 7, 2): + self.waypoint_mapping.update({f'waypoint{i}': 'right'}) + + def init_episode(self, index) -> List[str]: + self._variation_index = index + + self.register_success_conditions([LiftedCondition(self.ball, 0.95)]) + + return ['Lift the ball'] + + def variation_count(self) -> int: + return 1 #len(self._options) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_stick.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_stick.py new file mode 100644 index 0000000000000000000000000000000000000000..661eafd65f8aec96f1fa3e85a429d0c680c52e9d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_stick.py @@ -0,0 +1,47 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask +from rlbench.backend.conditions import Condition +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.spawn_boundary import SpawnBoundary + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + +class CoordinatedLiftStick(BimanualTask): + + def init_task(self) -> None: + + self.queue = Shape('queue') + self.register_graspable_objects([self.queue]) + + self.waypoint_mapping = defaultdict(lambda: 'right') + self.waypoint_mapping.update({'waypoint0': 'left', 'waypoint2': 'left', 'waypoint4': 'left', 'waypoint6': 'left'}) + + + def init_episode(self, index) -> List[str]: + self._variation_index = index + + self.register_success_conditions([LiftedCondition(self.queue, 1.2),]) + return ['lift the stick'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_tray.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_tray.py new file mode 100644 index 0000000000000000000000000000000000000000..076d5b9856866a63daebb4f45be001928dc06dc8 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_lift_tray.py @@ -0,0 +1,72 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from pyrep.objects.object import Object +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.conditions import Condition +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.spawn_boundary import SpawnBoundary + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + + +class CoordinatedLiftTray(BimanualTask): + + def init_task(self) -> None: + self.item = Shape('item') + self.tray = Shape('tray') + + self.register_graspable_objects([self.item]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + for i in range(0, 7, 2): + self.waypoint_mapping.update({f'waypoint{i}': 'right'}) + + def init_episode(self, index) -> List[str]: + + + self._variation_index = index + + right_sensor = ProximitySensor('Panda_rightArm_gripper_attachProxSensor') + left_sensor = ProximitySensor('Panda_leftArm_gripper_attachProxSensor') + + #tray_visual = Shape('tray_visual') + #print(self.item.get_position()) + #tray_visual.sample(self.item, min_distance=0.1, ignore_collisions=True) + self.item.set_position([0.0, 0.0, 0.001], relative_to=self.tray, reset_dynamics=False) + #print(self.item.get_position()) + + self.register_success_conditions([ + LiftedCondition(self.tray, 1.2), + LiftedCondition(self.item, 1.2), + DetectedCondition(self.tray, right_sensor), + DetectedCondition(self.tray, left_sensor)]) + + + + return ['Lift the tray'] + + def variation_count(self) -> int: + return 1 #len(self._options) + + def boundary_root(self) -> Object: + return Dummy('coordinated_lift_tray') + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_push_box.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_push_box.py new file mode 100644 index 0000000000000000000000000000000000000000..a257fb69d3173281ac01b5d5d0a945a8fde19c6a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_push_box.py @@ -0,0 +1,40 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask + +class CoordinatedPushBox(BimanualTask): + + def init_task(self) -> None: + + self.item = Shape('cube') + self.target = Shape('target') + self.register_graspable_objects([self.item]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint0': 'right', 'waypoint2': 'right'}) + + + def init_episode(self, index) -> List[str]: + self._variation_index = index + + #Dummy('waypoint0').set_position(position=(0,0,0),relative_to=self.target) + #Dummy('waypoint0').set_position(position=(0,0,0),relative_to=self.target) + + success_sensor = ProximitySensor('success0') + self.register_success_conditions([DetectedCondition(self.item, success_sensor)]) + return ['push the box to the red area'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + angle = np.deg2rad(10) + return [0, 0, - angle], [0, 0, angle] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_bottle_in_fridge.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_bottle_in_fridge.py new file mode 100644 index 0000000000000000000000000000000000000000..525f1e65300d83210d6af2ee044964d4f7bc38d9 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_bottle_in_fridge.py @@ -0,0 +1,56 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.conditions import NothingGrasped +from rlbench.backend.task import BimanualTask +from collections import defaultdict +from rlbench.backend.spawn_boundary import SpawnBoundary + +class CoordinatedPutBottleInFridge(BimanualTask): + + def init_task(self) -> None: + bottle = Shape('bottle') + self.register_graspable_objects([bottle]) + self.register_success_conditions( + [DetectedCondition(bottle, ProximitySensor('success')), + NothingGrasped(self.robot.right_gripper), NothingGrasped(self.robot.left_gripper)]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + for i in range(4): + self.waypoint_mapping[f'waypoint{i}'] = 'right' + + self.spawn_boundaries = [Shape('fridge_root')] + + def init_episode(self, index: int) -> List[str]: + + self._variation_index = index + + s = Shape('fridge_root') + s.set_position([ 0.05, -0.275, 0.752]) + print(s.get_position()) + + b = SpawnBoundary(self.spawn_boundaries) + b.sample(Shape('bottle'), min_distance=0.1) + + + return ['put bottle in fridge', + 'place the bottle inside the fridge', + 'open the fridge and put the bottle in there', + 'open the fridge door, pick up the bottle, and leave it in the ' + 'fridge'] + + def variation_count(self) -> int: + return 1 + + def boundary_root(self) -> Object: + return Shape('fridge_root') + + def is_static_workspace(self): + return True + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 4), (0.0, 0.0, np.pi / 4) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_item_in_drawer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_item_in_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..fb719334a24ee812d98745a30a31764df8d9d87c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_item_in_drawer.py @@ -0,0 +1,46 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask + +class CoordinatedPutItemInDrawer(BimanualTask): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + self._item = Shape('item') + self.register_graspable_objects([self._item]) + + self.waypoint_mapping = defaultdict(lambda: 'right') + self.waypoint_mapping.update({'waypoint0': 'left', 'waypoint1': 'left', 'waypoint2': 'left'}) + + + def init_episode(self, index) -> List[str]: + self._variation_index = index + option = self._options[index] + anchor = self._anchors[index] + self._waypoint1.set_position(anchor.get_position()) + success_sensor = ProximitySensor('success_' + option) + self.register_success_conditions( + [DetectedCondition(self._item, success_sensor)]) + return ['put the item in the %s drawer' % option, + 'put the block away in the %s drawer' % option, + 'open the %s drawer and place the block inside of it' % option, + 'leave the block in the %s drawer' % option] + + def variation_count(self) -> int: + return 3 # len(self._options) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_item_in_drawer_right.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_item_in_drawer_right.py new file mode 100644 index 0000000000000000000000000000000000000000..e0d0f386827af805936a647912c450a31ffedfa3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_put_item_in_drawer_right.py @@ -0,0 +1,46 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask + +class CoordinatedPutItemInDrawerRight(BimanualTask): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + self._item = Shape('item') + self.register_graspable_objects([self._item]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint0': 'right', 'waypoint1': 'right', 'waypoint2': 'right'}) + + + def init_episode(self, index) -> List[str]: + self._variation_index = index + option = self._options[index] + anchor = self._anchors[index] + self._waypoint1.set_position(anchor.get_position()) + success_sensor = ProximitySensor('success_' + option) + self.register_success_conditions( + [DetectedCondition(self._item, success_sensor)]) + return ['put the item in the %s drawer' % option, + 'put the block away in the %s drawer' % option, + 'open the %s drawer and place the block inside of it' % option, + 'leave the block in the %s drawer' % option] + + def variation_count(self) -> int: + return 3 #len(self._options) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_take_shoes_out_of_box.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_take_shoes_out_of_box.py new file mode 100644 index 0000000000000000000000000000000000000000..a669577e423710de32c1a0bca263d106104f7362 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_take_shoes_out_of_box.py @@ -0,0 +1,44 @@ +from typing import List, Tuple +import numpy as np +from collections import defaultdict +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.conditions import NothingGrasped +from rlbench.backend.task import BimanualTask + +class CoordinatedTakeShoesOutOfBox(BimanualTask): + + def init_task(self) -> None: + shoe1, shoe2 = Shape('shoe1'), Shape('shoe2') + self.register_graspable_objects([shoe1, shoe2]) + success_sensor = ProximitySensor('success_out_box') + self.register_success_conditions([ + DetectedCondition(shoe1, success_sensor), + DetectedCondition(shoe2, success_sensor), + NothingGrasped(self.robot.right_gripper), + NothingGrasped(self.robot.left_gripper)]) + + self.waypoint_mapping = defaultdict(lambda: 'right') + for i in range(4): + self.waypoint_mapping[f'waypoint{i}'] = 'left' + + def init_episode(self, index: int) -> List[str]: + return ['take shoes out of box', + 'open the shoe box and take the shoes out', + 'put the shoes found inside the box on the table', + 'set the shoes down on the table', + 'pick up the shoes from the box and put them down', + 'grasp the edge of the box lid to open it, then grasp each shoe' + ', lifting up out of the shoe box and leaving them down on the ' + 'table'] + + def variation_count(self) -> int: + return 1 + + def is_static_workspace(self): + return True + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_take_tray_out_of_oven.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_take_tray_out_of_oven.py new file mode 100644 index 0000000000000000000000000000000000000000..3b7ae7b66c9368cc199145a3166b1a25d749d7d1 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/coordinated_take_tray_out_of_oven.py @@ -0,0 +1,43 @@ +from collections import defaultdict +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.robot import BimanualRobot +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import BimanualTask + +class CoordinatedTakeTrayOutOfOven(BimanualTask): + + def init_task(self) -> None: + success_detector = ProximitySensor('success') + tray = Shape('tray') + assert(isinstance(self.robot, BimanualRobot)) + self.register_graspable_objects([tray]) + self.register_success_conditions( + [DetectedCondition(tray, success_detector, negated=True), + NothingGrasped(self.robot.right_gripper), NothingGrasped(self.robot.left_gripper)]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + for i in range(4): + self.waypoint_mapping[f'waypoint{i}'] = 'right' + + def init_episode(self, index: int) -> List[str]: + return ['take tray out of oven', + 'open the oven and take the baking tray out', + 'grasp the handle on the over door, open it, and remove the ' + 'tray from the oven', + 'get the baking tray from the oven', + 'get the tray', + 'take out the tray', + 'retrieve the oven tray'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 4.], [0, 0, 3.14 / 4.] + + def boundary_root(self) -> Object: + return Shape('oven_boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/dual_push_buttons.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/dual_push_buttons.py new file mode 100644 index 0000000000000000000000000000000000000000..c845af2cadb1687ea8e983e76eb925f2c112dfce --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/dual_push_buttons.py @@ -0,0 +1,151 @@ +from typing import List +import itertools +import math +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import JointCondition, ConditionSet +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +MAX_VARIATIONS = 50 + +# button top plate and wrapper will be be red before task completion +# and be changed to cyan upon success of task, so colors list used to randomly vary colors of +# base block will be redefined, excluding red and green +colors = [ + ('maroon', (0.5, 0.0, 0.0)), + ('green', (0.0, 0.5, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('navy', (0.0, 0.0, 0.5)), + ('yellow', (1.0, 1.0, 0.0)), + ('cyan', (0.0, 1.0, 1.0)), + ('magenta', (1.0, 0.0, 1.0)), + ('silver', (0.75, 0.75, 0.75)), + ('gray', (0.5, 0.5, 0.5)), + ('orange', (1.0, 0.5, 0.0)), + ('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + ('teal', (0, 0.5, 0.5)), + ('azure', (0.0, 0.5, 1.0)), + ('violet', (0.5, 0.0, 1.0)), + ('rose', (1.0, 0.0, 0.5)), + ('black', (0.0, 0.0, 0.0)), + ('white', (1.0, 1.0, 1.0)), +] + +#color_permutations = list(itertools.permutations(colors, 3)) + + +def print_permutations(color_permutations): + # pretty printing color_permutations for debug + print('num permutations: ', str(len(color_permutations))) + print('color_permutations:\n') + for i in range(len(color_permutations)): + print(str(color_permutations[i])) + if ((i + 1) % 16 == 0): print('') + + +class DualPushButtons(BimanualTask): + + + + def init_task(self) -> None: + self.buttons_pushed = 0 + self.color_variation_index = 0 + self.target_buttons = [Shape('push_buttons_target%d' % i) + for i in range(3)] + self.target_topPlates = [Shape('target_button_topPlate%d' % i) + for i in range(3)] + self.target_joints = [Joint('target_button_joint%d' % i) + for i in range(3)] + self.target_wraps = [Shape('target_button_wrap%d' % i) + for i in range(3)] + self.boundaries = Shape('push_buttons_boundary') + # goal_conditions merely state joint conditions for push action for + # each button regardless of whether the task involves pushing it + self.goal_conditions = [JointCondition(self.target_joints[n], 0.001) + for n in range(2)] + + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint0': 'right', 'waypoint2': 'right'}) + + def init_episode(self, index: int) -> List[str]: + + self._variation_index = index + + for tp in self.target_topPlates: + tp.set_color([1.0, 0.0, 0.0]) + for w in self.target_wraps: + w.set_color([1.0, 0.0, 0.0]) + + #button_colors = color_permutations[index] + + rng = np.random.default_rng(index) + button_colors = rng.choice(np.asarray(colors, dtype=object), 3, replace=False) + + self.color_names = [] + self.color_rgbs = [] + self.chosen_colors = [] + + for i, b in enumerate(self.target_buttons): + color_name, color_rgb = button_colors[i] + self.color_names.append(color_name) + self.color_rgbs.append(color_rgb) + self.chosen_colors.append((color_name, color_rgb)) + b.set_color(color_rgb) + + self.register_success_conditions([ConditionSet(self.goal_conditions, True, True)]) + + # ..todo separate the spawn boundaries for the left and right robot + + + right_boundary = Shape('push_buttons_boundary_right') + b = SpawnBoundary([right_boundary]) + b.sample(self.target_buttons[0], min_distance=0.1) + + left_boundary = Shape('push_buttons_boundary_left') + b = SpawnBoundary([left_boundary]) + b.sample(self.target_buttons[1], min_distance=0.1) + + b = SpawnBoundary([self.boundaries]) + b.sample(self.target_buttons[2], min_distance=0.1) + + + w0 = Dummy('waypoint0') + x, y, z = self.target_buttons[0].get_position() + w0.set_position([x, y, z + 0.083]) + w0.set_orientation([math.pi, 0, math.pi]) + + w0 = Dummy('waypoint1') + x, y, z = self.target_buttons[1].get_position() + w0.set_position([x, y, z + 0.083]) + w0.set_orientation([math.pi, 0, math.pi]) + + rtn0 = f'push the {self.color_names[0]} and the {self.color_names[1]} buttons' + rtn1 = f'press the {self.color_names[0]} and the {self.color_names[1]} buttons' + rtn2 = f'push down the buttons with the {self.color_names[0]} and the the {self.color_names[1]} base' + + + return [rtn0, rtn1, rtn2] + + def variation_count(self) -> int: + return MAX_VARIATIONS + + def cleanup(self) -> None: + self.buttons_pushed = 0 + + def base_rotation_bounds(self): + return [0.0] * 3, [0.0] * 3 + + def is_static_workspace(self): + return True + + def _move_above_next_target(self, waypoint): + if self.buttons_pushed >= self.buttons_to_push: + print('buttons_pushed:', self.buttons_pushed, 'buttons_to_push:', + self.buttons_to_push) + raise RuntimeError('Should not be here.') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item.py new file mode 100644 index 0000000000000000000000000000000000000000..672d9eeaa4bb825f62b423689d8068a7322e7a70 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item.py @@ -0,0 +1,101 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import BimanualTask +from rlbench.backend.spawn_boundary import SpawnBoundary +from pyrep.objects.dummy import Dummy +from rlbench.backend.exceptions import BoundaryError +from absl import logging +from pyrep.objects.object import Object +from rlbench.backend.conditions import Condition + + +colors = [ + ('red', (1.0, 0.0, 0.0)), + ('green', (0.0, 1.0, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('yellow', (1.0, 1.0, 0.0)), + #('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + #('teal', (0, 0.5, 0.5)), + #('black', (0.0, 0.0, 0.0)), + #('white', (1.0, 1.0, 1.0)), +] + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + +class HandoverItem(BimanualTask): + + def init_task(self) -> None: + + self.items = [Shape(f'item{i}') for i in range(5)] + + self.register_graspable_objects(self.items) + + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint0': 'right', 'waypoint5': 'right'}) + + self.boundaries = Shape('handover_item_boundary') + self.boundary_init_pose = Shape('handover_item_boundary').get_position() + + + def init_episode(self, index: int) -> List[str]: + + self._variation_index = index + + color_name, color = colors[index] + self.items[0].set_color(color) + + remaining_colors = colors.copy() + remaining_colors.remove((color_name, color)) + np.random.shuffle(remaining_colors) + + for i, item in enumerate(self.items[1:]): + item.set_color(remaining_colors[i][1]) + + Shape('handover_item_boundary').set_position(self.boundary_init_pose) + + b = SpawnBoundary([self.boundaries]) + b.MAX_SAMPLES = 1000 + b.clear() + for item in self.items: + try: + b.sample(item, min_distance=0.05) + except BoundaryError as err: + logging.warning("error %s. Sampling again while ignoring collisions ", err) + b.sample(item, ignore_collisions=True, min_distance=0.05) + + right_success_sensor = ProximitySensor('Panda_rightArm_gripper_attachProxSensor') + left_success_sensor = ProximitySensor('Panda_leftArm_gripper_attachProxSensor') + + self.register_success_conditions( + [DetectedCondition(self.items[0], right_success_sensor), + DetectedCondition(self.items[0], left_success_sensor, negated=True), + LiftedCondition(self.items[0], 0.8)]) + + return [f'bring me the {color_name} item', + f'hand over the {color_name} object'] + + def variation_count(self) -> int: + return len(colors) + + #def boundary_root(self) -> Object: + # return Shape('handover_item_boundary') + + def is_static_workspace(self): + return True + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item_easy.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item_easy.py new file mode 100644 index 0000000000000000000000000000000000000000..1fa25b9cacb9fa7be8e54b52fd7759e0c4bbb359 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item_easy.py @@ -0,0 +1,66 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.conditions import NothingGrasped +from rlbench.backend.task import BimanualTask +from rlbench.backend.spawn_boundary import SpawnBoundary +from pyrep.objects.dummy import Dummy +from pyrep.objects.object import Object +from rlbench.backend.conditions import Condition + + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + +class HandoverItemEasy(BimanualTask): + + def init_task(self) -> None: + + self.item = Shape('item') + + self.register_graspable_objects([self.item]) + + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint0': 'right', 'waypoint5': 'right'}) + + self.boundaries = Shape('handover_item_boundary') + + + def init_episode(self, index: int) -> List[str]: + + self._variation_index = index + + right_success_sensor = ProximitySensor('Panda_rightArm_gripper_attachProxSensor') + left_success_sensor = ProximitySensor('Panda_leftArm_gripper_attachProxSensor') + + #b = SpawnBoundary([self.boundaries]) + #b.clear() + # b.sample(item, min_distance=0.1) + + self.register_success_conditions( + [DetectedCondition(self.item, right_success_sensor), + DetectedCondition(self.item, left_success_sensor, negated=True), + LiftedCondition(self.item, 0.8)]) + + return [f'bring me the item', + f'hand over the object'] + + def variation_count(self) -> int: + return 1 + + def boundary_root(self) -> Object: + return Shape('handover_item_boundary') + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item_medium.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item_medium.py new file mode 100644 index 0000000000000000000000000000000000000000..6873ef7c358287e1b69b3bcfd8ffa4cdf2c3846d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/handover_item_medium.py @@ -0,0 +1,92 @@ +from collections import defaultdict +from typing import List, Tuple + +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import BimanualTask +from rlbench.backend.spawn_boundary import SpawnBoundary +from pyrep.objects.dummy import Dummy +from pyrep.objects.object import Object +from rlbench.backend.conditions import Condition + +colors = [ + ('red', (1.0, 0.0, 0.0)), + ('green', (0.0, 1.0, 0.0)), + ('blue', (0.0, 0.0, 1.0)), +] + +class LiftedCondition(Condition): + + def __init__(self, item: Shape, min_height: float): + self.item = item + self.min_height = min_height + + def condition_met(self): + pos = self.item.get_position() + return pos[2] >= self.min_height, False + +class HandoverItemMedium(BimanualTask): + + def init_task(self) -> None: + + self.items = [Shape(f'item{i}') for i in range(3)] + + for i, (_, color) in enumerate(colors): + self.items[i].set_color(color) + + self.register_graspable_objects(self.items) + + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint0': 'right', 'waypoint5': 'right'}) + + self.boundaries = Shape('handover_item_boundary') + + + def init_episode(self, index: int) -> List[str]: + + self._variation_index = index + + color_name, _color = colors[index] + + + b = SpawnBoundary([self.boundaries]) + b.clear() + for item in self.items: + b.sample(item, min_distance=0.1) + + + w0 = Dummy('waypoint2') + w0.set_position([0.0, 0.0, -0.025], relative_to=self.items[index], reset_dynamics=False) + #w0.set_orientation([-np.pi, 0, -np.pi], relative_to=self.items[index], reset_dynamics=False) + + w1 = Dummy('waypoint1') + w1.set_position([0.0, 0.0, 0.1], relative_to=self.items[index], reset_dynamics=False) + + w3 = Dummy('waypoint3') + w3.set_position([0.0, 0.0, 0.3], relative_to=self.items[index], reset_dynamics=False) + + + right_success_sensor = ProximitySensor('Panda_rightArm_gripper_attachProxSensor') + left_success_sensor = ProximitySensor('Panda_leftArm_gripper_attachProxSensor') + + self.register_success_conditions( + [DetectedCondition(self.items[index], right_success_sensor), + DetectedCondition(self.items[index], left_success_sensor, negated=True), + LiftedCondition(self.items[index], 0.8)]) + + return [f'bring me the {color_name} item', + f'hand over the {color_name} object'] + + def variation_count(self) -> int: + return len(colors) + + def boundary_root(self) -> Object: + return Shape('handover_item_boundary') + + def is_static_workspace(self): + return True + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/left_open_drawer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/left_open_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..d585a330d36ba2efe4da3b3f26f396f572caadfd --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/left_open_drawer.py @@ -0,0 +1,41 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +class LeftOpenDrawer(BimanualTask): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint3': 'right'}) + + + + def init_episode(self, index: int) -> List[str]: + + self._variation_index = index + + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + return ['open the %s drawer' % option, + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % option] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/right_open_drawer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/right_open_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..f65228a850c351abaceb61424b901f53082df46e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/right_open_drawer.py @@ -0,0 +1,39 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +class RightOpenDrawer(BimanualTask): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + self.waypoint_mapping = defaultdict(lambda: 'right') + self.waypoint_mapping.update({'waypoint3': 'left'}) + + def init_episode(self, index: int) -> List[str]: + + self._variation_index = index + + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + return ['open the %s drawer' % option, + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % option] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/unimanual_push_single_button_left.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/unimanual_push_single_button_left.py new file mode 100644 index 0000000000000000000000000000000000000000..390866149fcc0bd7050dd397ea94d88ef6b41119 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/bimanual_tasks/unimanual_push_single_button_left.py @@ -0,0 +1,176 @@ +from typing import List +import itertools +import math +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import JointCondition, ConditionSet +from rlbench.backend.task import BimanualTask +from collections import defaultdict + +MAX_TARGET_BUTTONS = 1 +MAX_VARIATIONS = 50 + +# button top plate and wrapper will be be red before task completion +# and be changed to cyan upon success of task, so colors list used to randomly vary colors of +# base block will be redefined, excluding red and green +colors = [ + ('maroon', (0.5, 0.0, 0.0)), + ('green', (0.0, 0.5, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('navy', (0.0, 0.0, 0.5)), + ('yellow', (1.0, 1.0, 0.0)), + ('cyan', (0.0, 1.0, 1.0)), + ('magenta', (1.0, 0.0, 1.0)), + ('silver', (0.75, 0.75, 0.75)), + ('gray', (0.5, 0.5, 0.5)), + ('orange', (1.0, 0.5, 0.0)), + ('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + ('teal', (0, 0.5, 0.5)), + ('azure', (0.0, 0.5, 1.0)), + ('violet', (0.5, 0.0, 1.0)), + ('rose', (1.0, 0.0, 0.5)), + ('black', (0.0, 0.0, 0.0)), + ('white', (1.0, 1.0, 1.0)), +] + +color_permutations = list(itertools.permutations(colors, 3)) + + +class UnimanualPushSingleButtonLeft(BimanualTask): + + + def init_task(self) -> None: + self.buttons_pushed = 0 + self.color_variation_index = 0 + self.target_buttons = [Shape('push_buttons_target%d' % i) + for i in range(3)] + self.target_topPlates = [Shape('target_button_topPlate%d' % i) + for i in range(3)] + self.target_joints = [Joint('target_button_joint%d' % i) + for i in range(3)] + self.target_wraps = [Shape('target_button_wrap%d' % i) + for i in range(3)] + self.boundaries = Shape('push_buttons_boundary') + # goal_conditions merely state joint conditions for push action for + # each button regardless of whether the task involves pushing it + self.goal_conditions = [JointCondition(self.target_joints[n], 0.001) + for n in range(3)] + + self.waypoint_mapping = defaultdict(lambda: 'left') + self.waypoint_mapping.update({'waypoint2': 'right'}) + + #left_tcp = Dummy('Panda_leftArm_tip') +# + #Dummy('waypoint2').set_position(position=(0,0,0), relative_to=left_tcp) + #Dummy('waypoint2').set_orientation(orientation=(0,0,0), relative_to=left_tcp) + + #self.register_waypoint_ability_start(0, self._move_above_next_target) + #self.register_waypoints_should_repeat(self._repeat) + + + def init_episode(self, index: int) -> List[str]: + + + for tp in self.target_topPlates: + tp.set_color([1.0, 0.0, 0.0]) + for w in self.target_wraps: + w.set_color([1.0, 0.0, 0.0]) + # For each color permutation, we want to have 1, 2 or 3 buttons pushed + color_index = int(index / MAX_TARGET_BUTTONS) + self.buttons_to_push = 1 + index % MAX_TARGET_BUTTONS + button_colors = color_permutations[color_index] + + self.color_names = [] + self.color_rgbs = [] + self.chosen_colors = [] + i = 0 + for b in self.target_buttons: + color_name, color_rgb = button_colors[i] + self.color_names.append(color_name) + self.color_rgbs.append(color_rgb) + self.chosen_colors.append((color_name, color_rgb)) + b.set_color(color_rgb) + i += 1 + + # for task success, all button to push must have green color RGB + self.success_conditions = [] + for i in range(self.buttons_to_push): + self.success_conditions.append(self.goal_conditions[i]) + + self.register_success_conditions( + [ConditionSet(self.success_conditions, True, False)]) + + rtn0 = 'push the %s button' % self.color_names[0] + rtn1 = 'press the %s button' % self.color_names[0] + rtn2 = 'push down the button with the %s base' % self.color_names[0] + for i in range(self.buttons_to_push): + if i == 0: + continue + else: + rtn0 += ', then push the %s button' % self.color_names[i] + rtn1 += ', then press the %s button' % self.color_names[i] + rtn2 += ', then the %s one' % self.color_names[i] + + b = SpawnBoundary([self.boundaries]) + for button in self.target_buttons: + b.sample(button, min_distance=0.1) + + num_non_targets = 3 - self.buttons_to_push + spare_colors = list(set(colors) + - set( + [self.chosen_colors[i] for i in range(self.buttons_to_push)])) + + spare_color_rgbs = [] + for i in range(len(spare_colors)): + _, rgb = spare_colors[i] + spare_color_rgbs.append(rgb) + + color_choice_indexes = np.random.choice(range(len(spare_colors)), + size=num_non_targets, + replace=False) + non_target_index = 0 + for i, button in enumerate(self.target_buttons): + if i in range(self.buttons_to_push): + pass + else: + _, rgb = spare_colors[color_choice_indexes[non_target_index]] + button.set_color(rgb) + non_target_index += 1 + + + left_tcp = Dummy('Panda_rightArm_tip') + Dummy('waypoint2').set_position(position=(0,0,0), relative_to=left_tcp) + Dummy('waypoint2').set_orientation(orientation=(0,0,0), relative_to=left_tcp) + + + + w0 = Dummy('waypoint0') + x, y, z = self.target_buttons[0].get_position() + w0.set_position([x, y, z + 0.083]) + w0.set_orientation([math.pi, 0, math.pi]) + + + + return [rtn0, rtn1, rtn2] + + def variation_count(self) -> int: + return np.minimum( + len(color_permutations) * MAX_TARGET_BUTTONS, MAX_VARIATIONS) + + def step(self) -> None: + for i in range(len(self.target_buttons)): + if self.goal_conditions[i].condition_met() == (True, True): + self.target_topPlates[i].set_color([0.0, 1.0, 0.0]) + self.target_wraps[i].set_color([0.0, 1.0, 0.0]) + + def cleanup(self) -> None: + self.buttons_pushed = 0 + + + def is_static_workspace(self): + return True \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/const.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/const.py new file mode 100644 index 0000000000000000000000000000000000000000..24ca2c221661d896e04dba540f44c80e446fe842 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/const.py @@ -0,0 +1,48 @@ +from pyrep.robots.arms.jaco import Jaco +from pyrep.robots.arms.mico import Mico +from pyrep.robots.arms.panda import Panda +from pyrep.robots.arms.dual_panda import PandaLeft, PandaRight +from pyrep.robots.arms.sawyer import Sawyer +from pyrep.robots.arms.ur5 import UR5 +from pyrep.robots.end_effectors.baxter_gripper import BaxterGripper +from pyrep.robots.end_effectors.jaco_gripper import JacoGripper +from pyrep.robots.end_effectors.mico_gripper import MicoGripper +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from pyrep.robots.end_effectors.robotiq85_gripper import Robotiq85Gripper +from pyrep.robots.end_effectors.dual_panda_gripper import PandaGripperLeft, PandaGripperRight + + + +colors = [ + ('red', (1.0, 0.0, 0.0)), + ('maroon', (0.5, 0.0, 0.0)), + ('lime', (0.0, 1.0, 0.0)), + ('green', (0.0, 0.5, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('navy', (0.0, 0.0, 0.5)), + ('yellow', (1.0, 1.0, 0.0)), + ('cyan', (0.0, 1.0, 1.0)), + ('magenta', (1.0, 0.0, 1.0)), + ('silver', (0.75, 0.75, 0.75)), + ('gray', (0.5, 0.5, 0.5)), + ('orange', (1.0, 0.5, 0.0)), + ('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + ('teal', (0, 0.5, 0.5)), + ('azure', (0.0, 0.5, 1.0)), + ('violet', (0.5, 0.0, 1.0)), + ('rose', (1.0, 0.0, 0.5)), + ('black', (0.0, 0.0, 0.0)), + ('white', (1.0, 1.0, 1.0)), +] + +# Arms from PyRep need to be modified to include a wrist camera. +# Currently, only the arms/grippers below are supported. +SUPPORTED_ROBOTS = { + 'panda': (Panda, PandaGripper, 7), + 'jaco': (Jaco, JacoGripper, 6), + 'mico': (Mico, MicoGripper, 6), + 'sawyer': (Sawyer, BaxterGripper, 7), + 'ur5': (UR5, Robotiq85Gripper, 6), + 'dual_panda': (PandaRight, PandaGripperRight, 14) +} diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/demo.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..c3ce0a1c648e0d56f2a7903f97ce9786e1986e79 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/demo.py @@ -0,0 +1,18 @@ +import numpy as np + + +class Demo(object): + + def __init__(self, observations, random_seed=None): + self._observations = observations + self.random_seed = random_seed + self.variation_number = 0 + + def __len__(self): + return len(self._observations) + + def __getitem__(self, i): + return self._observations[i] + + def restore_state(self): + np.random.set_state(self.random_seed) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/environment.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/environment.py new file mode 100644 index 0000000000000000000000000000000000000000..edc7c3ee50a25d8c19c5bb6384675b99c1c5d9ed --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/environment.py @@ -0,0 +1,239 @@ +import importlib +from os.path import exists, dirname, abspath, join +from typing import Type, List +import pickle + +from pyrep import PyRep +from pyrep.objects import VisionSensor +from pyrep.robots.arms.panda import Panda +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from pyrep.robots.arms.dual_panda import PandaLeft +from pyrep.robots.arms.dual_panda import PandaRight +from pyrep.robots.end_effectors.dual_panda_gripper import PandaGripperRight +from pyrep.robots.end_effectors.dual_panda_gripper import PandaGripperLeft + +from rlbench import utils +from rlbench.action_modes.action_mode import ActionMode +from rlbench.backend.const import * +from rlbench.backend.robot import Robot +from rlbench.backend.robot import UnimanualRobot +from rlbench.backend.robot import BimanualRobot +from rlbench.backend.scene import Scene +from rlbench.backend.task import Task +from rlbench.const import SUPPORTED_ROBOTS +from rlbench.demo import Demo +from rlbench.observation_config import ObservationConfig +from rlbench.sim2real.domain_randomization import RandomizeEvery, \ + VisualRandomizationConfig, DynamicsRandomizationConfig +from rlbench.sim2real.domain_randomization_scene import DomainRandomizationScene +from rlbench.task_environment import TaskEnvironment + +import logging + +DIR_PATH = dirname(abspath(__file__)) + + +class Environment(object): + """Each environment has a scene.""" + + def __init__(self, + action_mode: ActionMode, + dataset_root: str = '', + obs_config: ObservationConfig = ObservationConfig(), + headless: bool = False, + static_positions: bool = False, + robot_setup: str = 'panda', + randomize_every: RandomizeEvery = None, + frequency: int = 1, + visual_randomization_config: VisualRandomizationConfig = None, + dynamics_randomization_config: DynamicsRandomizationConfig = None, + attach_grasped_objects: bool = True, + shaped_rewards: bool = False + ): + + self._dataset_root = dataset_root + self._action_mode = action_mode + self._obs_config = obs_config + self._headless = headless + self._static_positions = static_positions + self._robot_setup = robot_setup.lower() + + self._randomize_every = randomize_every + self._frequency = frequency + self._visual_randomization_config = visual_randomization_config + self._dynamics_randomization_config = dynamics_randomization_config + self._attach_grasped_objects = attach_grasped_objects + self._shaped_rewards = shaped_rewards + + if robot_setup not in SUPPORTED_ROBOTS.keys(): + raise ValueError('robot_configuration must be one of %s' % + str(SUPPORTED_ROBOTS.keys())) + + if (randomize_every is not None and + visual_randomization_config is None and + dynamics_randomization_config is None): + raise ValueError( + 'If domain randomization is enabled, must supply either ' + 'visual_randomization_config or dynamics_randomization_config') + + self._check_dataset_structure() + self._pyrep = None + self._robot = None + self._scene = None + self._prev_task = None + + def _check_dataset_structure(self): + if len(self._dataset_root) > 0 and not exists(self._dataset_root): + raise RuntimeError( + 'Data set root does not exists: %s' % self._dataset_root) + + def _string_to_task(self, task_name: str): + task_name = task_name.replace('.py', '') + try: + class_name = ''.join( + [w[0].upper() + w[1:] for w in task_name.split('_')]) + mod = importlib.import_module("rlbench.tasks.%s" % task_name) + except Exception as e: + raise RuntimeError( + 'Tried to interpret %s as a task, but failed. Only valid tasks ' + 'should belong in the tasks/ folder' % task_name) from e + return getattr(mod, class_name) + + def launch(self): + if self._pyrep is not None: + raise RuntimeError('Already called launch!') + self._pyrep = PyRep() + if self._robot_setup == 'dual_panda': + self._pyrep.launch(join(DIR_PATH, BIMANUAL_TTT_FILE), headless=self._headless) + else: + self._pyrep.launch(join(DIR_PATH, TTT_FILE), headless=self._headless) + + arm_class, gripper_class, _ = SUPPORTED_ROBOTS[ + self._robot_setup] + + + if self._robot_setup == 'dual_panda': + + logging.info("Using dual panda robot") + + #panda_arm = Panda() + #panda_pos = panda_arm.get_position() + #panda_arm.remove() + + right_arm = PandaRight() + left_arm = PandaLeft() + right_gripper = PandaGripperRight() + left_gripper = PandaGripperLeft() + + # ..not updating position as we assume that the scene already contains two pandas which are placed correctly + #relative_left_position = left_arm.get_position(relative_to=right_arm) + #right_arm.set_position(panda_pos) + #left_arm.set_position(relative_left_position, relative_to=right_arm) + + self._robot = BimanualRobot(right_arm, right_gripper, left_arm, left_gripper) + + # We assume the panda is already loaded in the scene. + elif self._robot_setup != 'panda': + # Remove the panda from the scene + panda_arm = Panda() + panda_pos = panda_arm.get_position() + panda_arm.remove() + arm_path = join(DIR_PATH, 'robot_ttms', self._robot_setup + '.ttm') + self._pyrep.import_model(arm_path) + arm, gripper = arm_class(), gripper_class() + arm.set_position(panda_pos) + self._robot = UnimanualRobot(arm, gripper) + else: + arm, gripper = arm_class(), gripper_class() + self._robot = UnimanualRobot(arm, gripper) + + + if self._randomize_every is None: + self._scene = Scene( + self._pyrep, self._robot, self._obs_config, self._robot_setup) + else: + self._scene = DomainRandomizationScene( + self._pyrep, self._robot, self._obs_config, self._robot_setup, + self._randomize_every, self._frequency, + self._visual_randomization_config, + self._dynamics_randomization_config) + + self._action_mode.arm_action_mode.set_control_mode(self._robot) + + def shutdown(self): + if self._pyrep is not None: + self._pyrep.shutdown() + self._pyrep = None + + def get_task(self, task_class: Type[Task]) -> TaskEnvironment: + + # If user hasn't called launch, implicitly call it. + if self._pyrep is None: + self.launch() + self._scene.unload() + task = task_class(self._pyrep, self._robot) + self._prev_task = task + return TaskEnvironment( + self._pyrep, self._robot, self._scene, task, + self._action_mode, self._dataset_root, self._obs_config, + self._static_positions, self._attach_grasped_objects, + self._shaped_rewards) + + @property + def action_shape(self): + return self._action_mode.action_shape(self._scene), + + def get_demos(self, task_name: str, amount: int, + variation_number=0, + image_paths=False, + random_selection: bool = True, + from_episode_number: int = 0) -> List[Demo]: + if self._dataset_root is None or len(self._dataset_root) == 0: + raise RuntimeError( + "Can't ask for a stored demo when no dataset root provided.") + demos = utils.get_stored_demos( + amount, image_paths, self._dataset_root, variation_number, + task_name, self._obs_config, random_selection, from_episode_number) + return demos + + def get_task_descriptions_with_episode(self, task_name: str, + episode_number: int) -> List[str]: + episode_description_pkl_file = join(self._dataset_root, + f'{task_name}', + VARIATIONS_ALL_FOLDER, + EPISODES_FOLDER, + EPISODE_FOLDER % episode_number, + VARIATION_DESCRIPTIONS) + with open(episode_description_pkl_file, 'rb') as f: + episode_description = pickle.load(f) + + return episode_description + + def get_scene_data(self) -> dict: + """Get the data of various scene/camera information. + + This temporarily starts the simulator in headless mode. + + :return: A dictionary containing scene data. + """ + + def _get_cam_info(cam: VisionSensor): + if not cam.still_exists(): + logging.warning("Camera no longer exists") + return None + intrinsics = cam.get_intrinsic_matrix() + return dict( + intrinsics=intrinsics, + near_plane=cam.get_near_clipping_plane(), + far_plane=cam.get_far_clipping_plane(), + extrinsics=cam.get_matrix()) + + headless = self._headless + self._headless = True + self.launch() + + d = {camera_name: _get_cam_info(sensor) for camera_name, sensor in self._scene.camera_sensors} + + self.shutdown() + self._headless = headless + return d diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/gym/__init__.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/gym/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..26ff7f4380ee2e384e8c11b3dc8ee6c38147c055 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/gym/__init__.py @@ -0,0 +1,28 @@ +from gym.envs.registration import register +import rlbench.backend.task as task +import os +from rlbench.utils import name_to_task_class +from rlbench.gym.rlbench_env import RLBenchEnv + +TASKS = [t for t in os.listdir(task.TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + +for task_file in TASKS: + task_name = task_file.split('.py')[0] + task_class = name_to_task_class(task_name) + register( + id='%s-state-v0' % task_name, + entry_point='rlbench.gym:RLBenchEnv', + kwargs={ + 'task_class': task_class, + 'observation_mode': 'state' + } + ) + register( + id='%s-vision-v0' % task_name, + entry_point='rlbench.gym:RLBenchEnv', + kwargs={ + 'task_class': task_class, + 'observation_mode': 'vision' + } + ) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/gym/rlbench_env.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/gym/rlbench_env.py new file mode 100644 index 0000000000000000000000000000000000000000..2dbaccd4504d607f80fc1fe5cdac720d39ba967b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/gym/rlbench_env.py @@ -0,0 +1,115 @@ +from typing import Union, Dict, Tuple + +import gym +import numpy as np +from gym import spaces +from pyrep.const import RenderMode +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.environment import Environment +from rlbench.observation_config import ObservationConfig + + +class RLBenchEnv(gym.Env): + """An gym wrapper for RLBench.""" + + metadata = {'render.modes': ['human', 'rgb_array']} + + def __init__(self, task_class, observation_mode='state', + render_mode: Union[None, str] = None): + self._observation_mode = observation_mode + self._render_mode = render_mode + obs_config = ObservationConfig() + if observation_mode == 'state': + obs_config.set_all_high_dim(False) + obs_config.set_all_low_dim(True) + elif observation_mode == 'vision': + obs_config.set_all(True) + else: + raise ValueError( + 'Unrecognised observation_mode: %s.' % observation_mode) + + action_mode = MoveArmThenGripper(JointVelocity(), Discrete()) + self.env = Environment( + action_mode, obs_config=obs_config, headless=True) + self.env.launch() + self.task = self.env.get_task(task_class) + + _, obs = self.task.reset() + + self.action_space = spaces.Box( + low=-1.0, high=1.0, shape=self.env.action_shape) + + if observation_mode == 'state': + self.observation_space = spaces.Box( + low=-np.inf, high=np.inf, shape=obs.get_low_dim_data().shape) + elif observation_mode == 'vision': + observation_space = { + "state": spaces.Box( + low=-np.inf, high=np.inf, + shape=obs.get_low_dim_data().shape), + + } + for camera_name, config in obs_config.camera_config: + observation_space[camera_name] = spaces.Box( low=0, high=1, shape=config.shape) + + self.observation_space = spaces.Dict(observation_space) + + if render_mode is not None: + # Add the camera to the scene + cam_placeholder = Dummy('cam_cinematic_placeholder') + self._gym_cam = VisionSensor.create([640, 360]) + self._gym_cam.set_pose(cam_placeholder.get_pose()) + if render_mode == 'human': + self._gym_cam.set_render_mode(RenderMode.OPENGL3_WINDOWED) + else: + self._gym_cam.set_render_mode(RenderMode.OPENGL3) + + def _extract_obs(self, obs) -> Dict[str, np.ndarray]: + if self._observation_mode == 'state': + return obs.get_low_dim_data() + elif self._observation_mode == 'vision': + obs = {"state": obs.get_low_dim_data()} + + ..todo:: { + "state": obs.get_low_dim_data(), + "left_shoulder_rgb": obs.left_shoulder_rgb, + "right_shoulder_rgb": obs.right_shoulder_rgb, + "wrist_rgb": obs.wrist_rgb, + "front_rgb": obs.front_rgb, + } + + for camera_name, _ in self.env._obs_config.camera_config: + obs.update(obs.camera_data.get(camera_name)) + return obs + + + def render(self, mode='human') -> Union[None, np.ndarray]: + if mode != self._render_mode: + raise ValueError( + 'The render mode must match the render mode selected in the ' + 'constructor. \nI.e. if you want "human" render mode, then ' + 'create the env by calling: ' + 'gym.make("reach_target-state-v0", render_mode="human").\n' + 'You passed in mode %s, but expected %s.' % ( + mode, self._render_mode)) + if mode == 'rgb_array': + frame = self._gym_cam.capture_rgb() + frame = np.clip((frame * 255.).astype(np.uint8), 0, 255) + return frame + + def reset(self) -> Dict[str, np.ndarray]: + descriptions, obs = self.task.reset() + del descriptions # Not used. + return self._extract_obs(obs) + + def step(self, action) -> Tuple[Dict[str, np.ndarray], float, bool, dict]: + obs, reward, terminate = self.task.step(action) + return self._extract_obs(obs), reward, terminate, {} + + def close(self) -> None: + self.env.shutdown() diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/noise_model.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/noise_model.py new file mode 100644 index 0000000000000000000000000000000000000000..dc9d806f00e5ef80a07f5885b03f0964c4c6bf01 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/noise_model.py @@ -0,0 +1,28 @@ +from typing import Tuple + +import numpy as np + + +class NoiseModel(object): + + def apply(self, val: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + +class Identity(NoiseModel): + def apply(self, val: np.ndarray): + return val + + +class GaussianNoise(NoiseModel): + + def __init__(self, variance, min_max_clip: Tuple[float, float]=None): + self._variance = variance + self._min_max_clip = min_max_clip + + def apply(self, val: np.ndarray): + val += np.random.normal(0.0, scale=self._variance, size=val.shape) + if self._min_max_clip is not None: + val = np.clip(val, *self._min_max_clip) + return val + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/observation_config.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/observation_config.py new file mode 100644 index 0000000000000000000000000000000000000000..472188205c54efb360ea30b7484a7c2f8f33634b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/observation_config.py @@ -0,0 +1,99 @@ +from typing import Dict +from dataclasses import dataclass + +from pyrep.const import RenderMode +from rlbench.noise_model import NoiseModel, Identity + + +#@dataclass +class CameraConfig(object): + def __init__(self, + rgb=True, + rgb_noise: NoiseModel=Identity(), + depth=True, + depth_noise: NoiseModel=Identity(), + point_cloud=True, + mask=True, + image_size=(128, 128), + render_mode=RenderMode.OPENGL3, + masks_as_one_channel=True, + depth_in_meters=False): + self.rgb = rgb + self.rgb_noise = rgb_noise + self.depth = depth + self.depth_noise = depth_noise + self.point_cloud = point_cloud + self.mask = mask + self.image_size = image_size + self.render_mode = render_mode + self.masks_as_one_channel = masks_as_one_channel + self.depth_in_meters = depth_in_meters + + def set_all(self, value: bool): + self.rgb = value + self.depth = value + self.point_cloud = value + self.mask = value + + + +#@dataclass +class ObservationConfig(object): + + + def __init__(self, + camera_configs: Dict[str, CameraConfig] = None, + joint_velocities=True, + joint_velocities_noise: NoiseModel=Identity(), + joint_positions=True, + joint_positions_noise: NoiseModel=Identity(), + joint_forces=True, + joint_forces_noise: NoiseModel=Identity(), + gripper_open=True, + gripper_pose=True, + gripper_matrix=False, + gripper_joint_positions=False, + gripper_touch_forces=False, + wrist_camera_matrix=False, + record_gripper_closing=False, + task_low_dim_state=True, + record_ignore_collisions=True, + robot_name='' + ): + self.camera_configs = camera_configs or dict() + self.joint_velocities = joint_velocities + self.joint_velocities_noise = joint_velocities_noise + self.joint_positions = joint_positions + self.joint_positions_noise = joint_positions_noise + self.joint_forces = joint_forces + self.joint_forces_noise = joint_forces_noise + self.gripper_open = gripper_open + self.gripper_pose = gripper_pose + self.gripper_matrix = gripper_matrix + self.gripper_joint_positions = gripper_joint_positions + self.gripper_touch_forces = gripper_touch_forces + self.wrist_camera_matrix = wrist_camera_matrix + self.record_gripper_closing = record_gripper_closing + self.task_low_dim_state = task_low_dim_state + self.record_ignore_collisions = record_ignore_collisions + self.robot_name = robot_name + + def set_all(self, value: bool): + self.set_all_high_dim(value) + self.set_all_low_dim(value) + + def set_all_high_dim(self, value: bool): + for _, config in self.camera_configs: + config.set_all(value) + + def set_all_low_dim(self, value: bool): + self.joint_velocities = value + self.joint_positions = value + self.joint_forces = value + self.gripper_open = value + self.gripper_pose = value + self.gripper_matrix = value + self.gripper_joint_positions = value + self.gripper_touch_forces = value + self.wrist_camera_matrix = value + self.task_low_dim_state = value diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/__init__.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/domain_randomization.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/domain_randomization.py new file mode 100644 index 0000000000000000000000000000000000000000..a72cb83ef23bb82b191198563ec2ce0bab43b9d4 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/domain_randomization.py @@ -0,0 +1,85 @@ +from enum import Enum +from typing import List +import numpy as np +import os +import glob + + +class RandomizeEvery(Enum): + EPISODE = 0 + VARIATION = 1 + TRANSITION = 1 + + +class Distributions(object): + + def apply(self, val: np.ndarray) -> np.ndarray: + pass + + +class Gaussian(Distributions): + + def __init__(self, variance): + self._variance = variance + + def apply(self, val: np.ndarray): + return np.random.normal(val, self._variance) + + +class Uniform(Distributions): + + def __init__(self, min, max): + self._min = min + self._max = max + + def apply(self, val: np.ndarray): + return np.random.uniform(self._min, self._max, val.shape) + + +EXTENSIONS = ['*.jpg', '*.png'] + + +class RandomizationConfig(object): + + def __init__(self, + whitelist: List[str]=None, + blacklist: List[str]=None, + randomize_arm: bool = True): + self.whitelist = whitelist + self.blacklist = [] if blacklist is None else blacklist + self.randomize_arm = randomize_arm + + def should_randomize(self, obj_name: str): + return ((self.whitelist is None and len(self.blacklist) == 0) or + (self.whitelist is not None and obj_name in self.whitelist) or + (obj_name not in self.blacklist)) and ( + self.randomize_arm or 'panda' not in obj_name.lower()) + + +class DynamicsRandomizationConfig(RandomizationConfig): + pass + + +class VisualRandomizationConfig(RandomizationConfig): + + def __init__(self, + image_directory: str, + whitelist: List[str] = None, + blacklist: List[str] = None, + randomize_arm: bool = True): + super().__init__(whitelist, blacklist, randomize_arm) + self._image_directory = image_directory + if not os.path.exists(image_directory): + raise NotADirectoryError( + 'The supplied image directory (%s) does not exist!' % + image_directory) + self._imgs = [glob.glob( + os.path.join(image_directory, e)) for e in EXTENSIONS] + self._imgs = np.concatenate(self._imgs) + if len(self._imgs) == 0: + raise RuntimeError( + 'The supplied image directory (%s) does not have any images!' % + image_directory) + + def sample(self, samples: int) -> np.ndarray: + return np.random.choice(self._imgs, samples) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/domain_randomization_scene.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/domain_randomization_scene.py new file mode 100644 index 0000000000000000000000000000000000000000..9ea7cee39963c0aaa9c57e7b2e54e9958315e68c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/sim2real/domain_randomization_scene.py @@ -0,0 +1,101 @@ +from typing import List +from pyrep import PyRep +from pyrep.const import ObjectType, TextureMappingMode +from pyrep.objects.shape import Shape + +from rlbench.backend.scene import Scene +from rlbench.observation_config import ObservationConfig +from rlbench.backend.robot import Robot +from rlbench.sim2real.domain_randomization import RandomizeEvery + +SCENE_OBJECTS = ['Floor', 'Roof', 'Wall1', 'Wall2', 'Wall3', 'Wall4', + 'diningTable_visible'] + +TEX_KWARGS = { + 'mapping_mode': TextureMappingMode.PLANE, + 'repeat_along_u': True, + 'repeat_along_v': True, + 'uv_scaling': [4., 4.] +} + + +class DomainRandomizationScene(Scene): + + def __init__(self, + pyrep: PyRep, + robot: Robot, + obs_config: ObservationConfig = ObservationConfig(), + robot_setup: str = 'Panda', + randomize_every: RandomizeEvery=RandomizeEvery.EPISODE, + frequency: int=1, + visual_randomization_config=None, + dynamics_randomization_config=None): + super().__init__(pyrep, robot, obs_config, robot_setup) + self._randomize_every = randomize_every + self._frequency = frequency + self._visual_rand_config = visual_randomization_config + self._dynamics_rand_config = dynamics_randomization_config + self._previous_index = -1 + self._count = 0 + + if self._dynamics_rand_config is not None: + raise NotImplementedError( + 'Dynamics randomization coming soon! ' + 'Only visual randomization available.') + + self._scene_objects = [Shape(name) for name in SCENE_OBJECTS] + self._scene_objects += self.robot.arm.get_visuals() + self._scene_objects += self.robot.gripper.get_visuals() + if self._visual_rand_config is not None: + # Make the floor plane renderable (to cover old floor) + self._scene_objects[0].set_position([0, 0, 0.01]) + self._scene_objects[0].set_renderable(True) + + def _should_randomize_episode(self, index: int): + rand = self._count % self._frequency == 0 or self._count == 0 + if self._randomize_every == RandomizeEvery.VARIATION: + if self._previous_index != index: + self._previous_index = index + self._count += 1 + elif self._randomize_every == RandomizeEvery.EPISODE: + self._count += 1 + return rand + + def _randomize(self): + tree = self.task.get_base().get_objects_in_tree( + ObjectType.SHAPE) + tree = [Shape(obj.get_handle()) for obj in tree + self._scene_objects] + if self._visual_rand_config is not None: + files = self._visual_rand_config.sample(len(tree)) + for file, obj in zip(files, tree): + if self._visual_rand_config.should_randomize(obj.get_name()): + text_ob, texture = self.pyrep.create_texture(file) + try: + obj.set_texture(texture, **TEX_KWARGS) + except RuntimeError: + ungrouped = obj.ungroup() + for o in ungrouped: + o.set_texture(texture, **TEX_KWARGS) + self.pyrep.group_objects(ungrouped) + text_ob.remove() + + def init_task(self) -> None: + super().init_task() + + def init_episode(self, index: int, *args, **kwargs) -> List[str]: + ret = super().init_episode(index, *args, **kwargs) + if (self._randomize_every != RandomizeEvery.TRANSITION and + self._should_randomize_episode(index)): + self._randomize() + self.pyrep.step() # Need to step to apply textures + return ret + + def step(self): + if self._randomize_every == RandomizeEvery.TRANSITION: + if self._count % self._frequency == 0 or self._count == 0 : + self._randomize() + self._count += 1 + super().step() + + def reset(self) -> None: + return super().reset() diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/task_environment.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/task_environment.py new file mode 100644 index 0000000000000000000000000000000000000000..908df229128de17ec00aa8b4e0735257e0ba5da1 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/task_environment.py @@ -0,0 +1,191 @@ +import logging +from typing import List, Callable + +import numpy as np +from pyrep import PyRep +from pyrep.const import ObjectType +from rlbench import utils +from rlbench.action_modes.action_mode import ActionMode +from rlbench.backend.exceptions import BoundaryError, WaypointError, \ + TaskEnvironmentError +from rlbench.backend.observation import Observation +from rlbench.backend.robot import Robot, UnimanualRobot, BimanualRobot +from rlbench.backend.scene import Scene +from rlbench.backend.task import Task +from rlbench.demo import Demo +from rlbench.observation_config import ObservationConfig + +_DT = 0.05 +_MAX_RESET_ATTEMPTS = 40 +_MAX_DEMO_ATTEMPTS = 10 + + +class TaskEnvironment(object): + + def __init__(self, + pyrep: PyRep, + robot: Robot, + scene: Scene, + task: Task, + action_mode: ActionMode, + dataset_root: str, + obs_config: ObservationConfig, + static_positions: bool = False, + attach_grasped_objects: bool = True, + shaped_rewards: bool = False + ): + self._pyrep = pyrep + self._robot = robot + self._scene = scene + self._task = task + self._variation_number = 0 + self._action_mode = action_mode + self._dataset_root = dataset_root + self._obs_config = obs_config + self._static_positions = static_positions + self._attach_grasped_objects = attach_grasped_objects + self._shaped_rewards = shaped_rewards + self._reset_called = False + self._prev_ee_velocity = None + self._enable_path_observations = False + + self._scene.load(self._task) + self._pyrep.start() + + if self._robot.is_bimanual: + #..fixme + logging.warning("not sure how _robot_shapes are used is used.") + self._robot_shapes = self._robot.right_arm.get_objects_in_tree(object_type=ObjectType.SHAPE) + self._robot.left_arm.get_objects_in_tree(object_type=ObjectType.SHAPE) + else: + self._robot_shapes = self._robot.arm.get_objects_in_tree(object_type=ObjectType.SHAPE) + + + def get_name(self) -> str: + return self._task.get_name() + + def sample_variation(self) -> int: + self._variation_number = np.random.randint( + 0, self._task.variation_count()) + return self._variation_number + + def set_variation(self, v: int) -> None: + if v >= self.variation_count(): + raise TaskEnvironmentError( + 'Requested variation %d, but there are only %d variations.' % ( + v, self.variation_count())) + self._variation_number = v + + def variation_count(self) -> int: + return self._task.variation_count() + + def reset(self) -> (List[str], Observation): + self._scene.reset() + try: + desc = self._scene.init_episode( + self._variation_number, max_attempts=_MAX_RESET_ATTEMPTS, + randomly_place=not self._static_positions) + except (BoundaryError, WaypointError) as e: + logging.error("error while initializing episode: %s", e) + raise TaskEnvironmentError( + 'Could not place the task %s in the scene. This should not ' + 'happen, please raise an issues on this task.' + % self._task.get_name()) from e + + self._reset_called = True + # Returns a list of descriptions and the first observation + return desc, self._scene.get_observation() + + def get_task_descriptions(self) -> List[str]: + return self._scene.task.init_episode(self._variation_number) + + def get_observation(self) -> Observation: + return self._scene.get_observation() + + def step(self, action) -> (Observation, int, bool): + # returns observation, reward, done, info + if not self._reset_called: + raise RuntimeError( + "Call 'reset' before calling 'step' on a task.") + self._action_mode.action(self._scene, action) + success, terminate = self._task.success() + reward = float(success) + if self._shaped_rewards: + reward = self._task.reward() + if reward is None: + raise RuntimeError( + 'User requested shaped rewards, but task %s does not have ' + 'a defined reward() function.' % self._task.get_name()) + return self._scene.get_observation(), reward, terminate + + def get_demos(self, amount: int, live_demos: bool = False, + image_paths: bool = False, + callable_each_step: Callable[[Observation], None] = None, + max_attempts: int = _MAX_DEMO_ATTEMPTS, + random_selection: bool = True, + from_episode_number: int = 0 + ) -> List[Demo]: + """Negative means all demos""" + + if not live_demos and (self._dataset_root is None + or len(self._dataset_root) == 0): + raise RuntimeError( + "Can't ask for a stored demo when no dataset root provided.") + + + if not live_demos: + if self._dataset_root is None or len(self._dataset_root) == 0: + raise RuntimeError( + "Can't ask for stored demo when no dataset root provided.") + demos = utils.get_stored_demos( + amount, image_paths, self._dataset_root, self._variation_number, + self._task.get_name(), self._obs_config, + random_selection, from_episode_number) + elif not self._robot.is_bimanual: + ctr_loop = self._robot.arm.joints[0].is_control_loop_enabled() + self._robot.arm.set_control_loop_enabled(True) + demos = self._get_live_demos( + amount, callable_each_step, max_attempts) + self._robot.arm.set_control_loop_enabled(ctr_loop) + elif self._robot.is_bimanual: + ctr_loop_right = self._robot.right_arm.joints[0].is_control_loop_enabled() + ctr_loop_left = self._robot.left_arm.joints[0].is_control_loop_enabled() + self._robot.right_arm.set_control_loop_enabled(True) + self._robot.left_arm.set_control_loop_enabled(True) + demos = self._get_live_demos( + amount, callable_each_step, max_attempts) + self._robot.right_arm.set_control_loop_enabled(ctr_loop_right) + self._robot.left_arm.set_control_loop_enabled(ctr_loop_left) + + return demos + + def _get_live_demos(self, amount: int, + callable_each_step: Callable[ + [Observation], None] = None, + max_attempts: int = _MAX_DEMO_ATTEMPTS) -> List[Demo]: + demos = [] + for i in range(amount): + attempts = max_attempts + while attempts > 0: + random_seed = np.random.get_state() + self.reset() + try: + demo = self._scene.get_demo( + callable_each_step=callable_each_step) + demo.random_seed = random_seed + demos.append(demo) + break + except Exception as e: + attempts -= 1 + logging.warning('Bad demo. ' + str(e) + ' Attempts left: ' + str(attempts)) + if attempts <= 0: + raise RuntimeError( + 'Could not collect demos. Maybe a problem with the task?') + return demos + + def reset_to_demo(self, demo: Demo) -> (List[str], Observation): + demo.restore_state() + # do not set variation as suggested in commit 6e79c5bac. 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b/third_party/AnyBimanual/third_party/RLBench/rlbench/task_ttms/wipe_desk.ttm new file mode 100644 index 0000000000000000000000000000000000000000..4900b41f2a6b5e4a17fdc4df572397fca672758c Binary files /dev/null and b/third_party/AnyBimanual/third_party/RLBench/rlbench/task_ttms/wipe_desk.ttm differ diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/__init__.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8be7d66dcd423a61a25bd36872b80e9636fe7c60 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/__init__.py @@ -0,0 +1,276 @@ +from rlbench.tasks.basketball_in_hoop import BasketballInHoop +from rlbench.tasks.beat_the_buzz import BeatTheBuzz +from rlbench.tasks.block_pyramid import BlockPyramid +from rlbench.tasks.change_channel import ChangeChannel +from rlbench.tasks.change_clock import ChangeClock +from rlbench.tasks.close_box import CloseBox +from rlbench.tasks.close_door import CloseDoor +from rlbench.tasks.close_drawer import CloseDrawer +from rlbench.tasks.close_fridge import CloseFridge +from rlbench.tasks.close_grill import CloseGrill +from rlbench.tasks.close_jar import CloseJar +from rlbench.tasks.close_laptop_lid import CloseLaptopLid +from rlbench.tasks.close_microwave import CloseMicrowave +from rlbench.tasks.empty_container import EmptyContainer +from rlbench.tasks.empty_dishwasher import EmptyDishwasher +from rlbench.tasks.get_ice_from_fridge import GetIceFromFridge +from rlbench.tasks.hang_frame_on_hanger import HangFrameOnHanger +from rlbench.tasks.hit_ball_with_queue import HitBallWithQueue +from rlbench.tasks.hockey import Hockey +from rlbench.tasks.insert_onto_square_peg import InsertOntoSquarePeg +from rlbench.tasks.insert_usb_in_computer import InsertUsbInComputer +from rlbench.tasks.lamp_off import LampOff +from rlbench.tasks.lamp_on import LampOn +from rlbench.tasks.lift_numbered_block import LiftNumberedBlock +from rlbench.tasks.light_bulb_in import LightBulbIn +from rlbench.tasks.light_bulb_out import LightBulbOut +from rlbench.tasks.meat_off_grill import MeatOffGrill +from rlbench.tasks.meat_on_grill import MeatOnGrill +from rlbench.tasks.move_hanger import MoveHanger +from rlbench.tasks.open_box import OpenBox +from rlbench.tasks.open_door import OpenDoor +from rlbench.tasks.open_drawer import OpenDrawer +from rlbench.tasks.open_fridge import OpenFridge +from rlbench.tasks.open_grill import OpenGrill +from rlbench.tasks.open_jar import OpenJar +from rlbench.tasks.open_microwave import OpenMicrowave +from rlbench.tasks.open_oven import OpenOven +from rlbench.tasks.open_washing_machine import OpenWashingMachine +from rlbench.tasks.open_window import OpenWindow +from rlbench.tasks.open_wine_bottle import OpenWineBottle +from rlbench.tasks.phone_on_base import PhoneOnBase +from rlbench.tasks.pick_and_lift import PickAndLift +from rlbench.tasks.pick_and_lift_small import PickAndLiftSmall +from rlbench.tasks.pick_up_cup import PickUpCup +from rlbench.tasks.place_cups import PlaceCups +from rlbench.tasks.place_hanger_on_rack import PlaceHangerOnRack +from rlbench.tasks.place_shape_in_shape_sorter import PlaceShapeInShapeSorter +from rlbench.tasks.play_jenga import PlayJenga +from rlbench.tasks.plug_charger_in_power_supply import PlugChargerInPowerSupply +from rlbench.tasks.pour_from_cup_to_cup import PourFromCupToCup +from rlbench.tasks.press_switch import PressSwitch +from rlbench.tasks.push_button import PushButton +from rlbench.tasks.push_buttons import PushButtons +from rlbench.tasks.put_all_groceries_in_cupboard import \ + PutAllGroceriesInCupboard +from rlbench.tasks.put_books_on_bookshelf import PutBooksOnBookshelf +from rlbench.tasks.put_bottle_in_fridge import PutBottleInFridge +from rlbench.tasks.put_groceries_in_cupboard import PutGroceriesInCupboard +from rlbench.tasks.put_item_in_drawer import PutItemInDrawer +from rlbench.tasks.put_knife_in_knife_block import PutKnifeInKnifeBlock +from rlbench.tasks.put_knife_on_chopping_board import PutKnifeOnChoppingBoard +from rlbench.tasks.put_money_in_safe import PutMoneyInSafe +from rlbench.tasks.put_plate_in_colored_dish_rack import \ + PutPlateInColoredDishRack +from rlbench.tasks.put_rubbish_in_bin import PutRubbishInBin +from rlbench.tasks.put_shoes_in_box import PutShoesInBox +from rlbench.tasks.put_toilet_roll_on_stand import PutToiletRollOnStand +from rlbench.tasks.put_tray_in_oven import PutTrayInOven +from rlbench.tasks.put_umbrella_in_umbrella_stand import \ + PutUmbrellaInUmbrellaStand +from rlbench.tasks.reach_and_drag import ReachAndDrag +from rlbench.tasks.reach_target import ReachTarget +from rlbench.tasks.remove_cups import RemoveCups +from rlbench.tasks.scoop_with_spatula import ScoopWithSpatula +from rlbench.tasks.screw_nail import ScrewNail +from rlbench.tasks.set_the_table import SetTheTable +from rlbench.tasks.setup_checkers import SetupCheckers +from rlbench.tasks.setup_chess import SetupChess +from rlbench.tasks.slide_block_to_target import SlideBlockToTarget +from rlbench.tasks.slide_cabinet_open import SlideCabinetOpen +from rlbench.tasks.slide_cabinet_open_and_place_cups import \ + SlideCabinetOpenAndPlaceCups +from rlbench.tasks.solve_puzzle import SolvePuzzle +from rlbench.tasks.stack_blocks import StackBlocks +from rlbench.tasks.stack_chairs import StackChairs +from rlbench.tasks.stack_cups import StackCups +from rlbench.tasks.stack_wine import StackWine +from rlbench.tasks.straighten_rope import StraightenRope +from rlbench.tasks.sweep_to_dustpan import SweepToDustpan +from rlbench.tasks.take_cup_out_from_cabinet import TakeCupOutFromCabinet +from rlbench.tasks.take_frame_off_hanger import TakeFrameOffHanger +from rlbench.tasks.take_item_out_of_drawer import TakeItemOutOfDrawer +from rlbench.tasks.take_lid_off_saucepan import TakeLidOffSaucepan +from rlbench.tasks.take_money_out_safe import TakeMoneyOutSafe +from rlbench.tasks.take_off_weighing_scales import TakeOffWeighingScales +from rlbench.tasks.take_plate_off_colored_dish_rack import \ + TakePlateOffColoredDishRack +from rlbench.tasks.take_shoes_out_of_box import TakeShoesOutOfBox +from rlbench.tasks.take_toilet_roll_off_stand import TakeToiletRollOffStand +from rlbench.tasks.take_tray_out_of_oven import TakeTrayOutOfOven +from rlbench.tasks.take_umbrella_out_of_umbrella_stand import \ + TakeUmbrellaOutOfUmbrellaStand +from rlbench.tasks.take_usb_out_of_computer import TakeUsbOutOfComputer +from rlbench.tasks.toilet_seat_down import ToiletSeatDown +from rlbench.tasks.toilet_seat_up import ToiletSeatUp +from rlbench.tasks.turn_oven_on import TurnOvenOn +from rlbench.tasks.turn_tap import TurnTap +from rlbench.tasks.tv_on import TvOn +from rlbench.tasks.unplug_charger import UnplugCharger +from rlbench.tasks.water_plants import WaterPlants +from rlbench.tasks.weighing_scales import WeighingScales +from rlbench.tasks.wipe_desk import WipeDesk + +# new tasks added in the PerAct paper +from rlbench.tasks.put_rubbish_in_color_bin import PutRubbishInColorBin +from rlbench.tasks.place_wine_at_rack_location import PlaceWineAtRackLocation +from rlbench.tasks.slide_block_to_color_target import SlideBlockToColorTarget +from rlbench.tasks.sweep_to_dustpan_of_size import SweepToDustpanOfSize +from rlbench.tasks.put_books_at_shelf_location import PutBooksAtShelfLocation +from rlbench.tasks.set_clock_to_time import SetClockToTime + +# analysis tasks +from rlbench.tasks.open_drawer_test_color_frame import OpenDrawerTestColorFrame +from rlbench.tasks.open_drawer_test_color_full import OpenDrawerTestColorFull +from rlbench.tasks.open_drawer_test_texture_frame import OpenDrawerTestTextureFrame +from rlbench.tasks.open_drawer_test_texture_full import OpenDrawerTestTextureFull + +from rlbench.tasks.open_drawer_test_shape_small_body import OpenDrawerTestShapeSmallBody +from rlbench.tasks.open_drawer_test_shape_square_handles import OpenDrawerTestShapeSquareHandles +from rlbench.tasks.open_drawer_test_shape_large_handles import OpenDrawerTestShapeLargeHandles + + +FS10_V1 = { + 'train': [ + ReachTarget, + CloseBox, + CloseMicrowave, + PlugChargerInPowerSupply, + ToiletSeatDown, + TakeUmbrellaOutOfUmbrellaStand, + PutUmbrellaInUmbrellaStand, + SlideCabinetOpen, + CloseFridge, + PickAndLift + ], + 'test': [ + OpenBox, + OpenMicrowave, + UnplugCharger, + ToiletSeatUp, + OpenFridge, + ] +} + +FS25_V1 = { + 'train': FS10_V1['train'] + FS10_V1['test'] + [ + TurnTap, + LightBulbIn, + BasketballInHoop, + OpenWindow, + CloseDoor, + PushButton, + PutItemInDrawer, + OpenDrawer, + CloseDrawer, + TurnOvenOn + ], + 'test': [ + LightBulbOut, + TvOn, + OpenOven, + OpenDoor, + TakeItemOutOfDrawer + ] +} + +FS50_V1 = { + 'train': FS25_V1['train'] + FS25_V1['test'] + [ + BeatTheBuzz, + BlockPyramid, + ChangeClock, + CloseJar, + CloseLaptopLid, + EmptyContainer, + EmptyDishwasher, + GetIceFromFridge, + HangFrameOnHanger, + InsertOntoSquarePeg, + PutRubbishInBin, + PutShoesInBox, + PutToiletRollOnStand, + PutTrayInOven, + ReachAndDrag, + RemoveCups, + ScoopWithSpatula, + SetTheTable, + SetupCheckers, + SlideBlockToTarget + ], + 'test': [ + Hockey, + InsertUsbInComputer, + PressSwitch, + PlayJenga, + MeatOffGrill, + ] +} + +FS95_V1 = { + 'train': FS50_V1['train'] + FS50_V1['test'] + [ + HitBallWithQueue, + ScrewNail, + LampOff, + LampOn, + MeatOnGrill, + MoveHanger, + OpenJar, + OpenWineBottle, + PlaceCups, + PlaceHangerOnRack, + PlaceShapeInShapeSorter, + PutBottleInFridge, + PutKnifeInKnifeBlock, + PutMoneyInSafe, + PutPlateInColoredDishRack, + SlideCabinetOpenAndPlaceCups, + StackBlocks, + StackCups, + StackWine, + StraightenRope, + SweepToDustpan, + TakeCupOutFromCabinet, + TakeFrameOffHanger, + TakeLidOffSaucepan, + TakeMoneyOutSafe, + TakeOffWeighingScales, + TakePlateOffColoredDishRack, + TakeShoesOutOfBox, + TakeToiletRollOffStand, + TakeUsbOutOfComputer, + WaterPlants, + WeighingScales, + WipeDesk, + ChangeChannel, + OpenGrill, + CloseGrill, + SolvePuzzle, + PickUpCup, + PhoneOnBase, + PourFromCupToCup + ], + 'test': [ + PutKnifeOnChoppingBoard, + PutBooksOnBookshelf, + PushButtons, + PutGroceriesInCupboard, + TakeTrayOutOfOven, + ] +} + +MT15_V1 = { + 'train': FS10_V1['train'] + FS10_V1['test'] +} + + +MT30_V1 = { + 'train': FS25_V1['train'] + FS25_V1['test'] +} + +MT55_V1 = { + 'train': FS50_V1['train'] + FS50_V1['test'] +} + +MT100_V1 = { + 'train': FS95_V1['train'] + FS95_V1['test'] +} diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/basketball_in_hoop.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/basketball_in_hoop.py new file mode 100644 index 0000000000000000000000000000000000000000..3bb8b11130c1899e6591be7321cf5c5dbaf35fad --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/basketball_in_hoop.py @@ -0,0 +1,34 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task + + +class BasketballInHoop(Task): + + def init_task(self): + ball = Shape('ball') + self.register_graspable_objects([ball]) + self.register_success_conditions( + [DetectedCondition(ball, ProximitySensor('success'))]) + + def init_episode(self, index: int) -> List[str]: + return ['put the ball in the hoop', + 'play basketball', + 'shoot the ball through the net', + 'pick up the basketball and put it in the hoop', + 'throw the basketball through the hoop', + 'place the basket ball through the hoop'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 4], [0, 0, np.pi / 4] + + def boundary_root(self) -> Object: + return Shape('basket_boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/beat_the_buzz.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/beat_the_buzz.py new file mode 100644 index 0000000000000000000000000000000000000000..7a3b63831d2a82a36954ce77c96b8a84bd49cb3b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/beat_the_buzz.py @@ -0,0 +1,36 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class BeatTheBuzz(Task): + + def init_task(self) -> None: + middle_sensor = ProximitySensor('middle_sensor') + right_sensor = ProximitySensor('right_sensor') + wand = Shape('wand') + self.register_graspable_objects([wand]) + self.register_fail_conditions( + [DetectedCondition(wand, middle_sensor)]) + self.register_success_conditions( + [DetectedCondition(wand, right_sensor), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['beat the buzz', + 'slide the ring along the pole without allowing them to touch', + 'slide the ring along the metal pole, avoiding contact between ' + 'them', + 'slide the ring from one end of the pole to the other without ' + 'allowing them to touch', + 'slide the ring from one end of the pole to the other, ' + 'avoiding contact between them', + 'move the ring from one end of the pole to the other, ' + 'maintaining a gap between them', + 'move the ring from end of the pole to the other whilst ' + 'maintaining separation between them'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/block_pyramid.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/block_pyramid.py new file mode 100644 index 0000000000000000000000000000000000000000..f5146f4f88bdfaba3ed2c70be1736f7fa8514f4a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/block_pyramid.py @@ -0,0 +1,61 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import (DetectedSeveralCondition, + NothingGrasped, ConditionSet) +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task +from rlbench.const import colors + + +class BlockPyramid(Task): + + def init_task(self) -> None: + self.blocks = [Shape('block_pyramid_block%d' % i) for i in range(6)] + self.distractors = [Shape( + 'block_pyramid_distractor_block%d' % i) for i in range(6)] + success_detectors = [ProximitySensor( + 'block_pyramid_success_block%d' % i) for i in range(3)] + + cond_set = ConditionSet([ + DetectedSeveralCondition(self.blocks, success_detectors[0], 3), + DetectedSeveralCondition(self.blocks, success_detectors[1], 2), + DetectedSeveralCondition(self.blocks, success_detectors[2], 1), + NothingGrasped(self.robot.gripper) + ]) + self.register_success_conditions([cond_set]) + self.register_graspable_objects(self.blocks + self.distractors) + self.spawn_boundary = SpawnBoundary( + [Shape('block_pyramid_boundary%d' % i) for i in range(4)]) + + def init_episode(self, index: int) -> List[str]: + + color_name, color_rgb = colors[index] + for obj in self.blocks: + obj.set_color(color_rgb) + + color_choice = np.random.choice( + list(range(index)) + list(range(index + 1, len(colors))), + size=1, replace=False)[0] + name, rgb = colors[color_choice] + for obj in self.distractors: + obj.set_color(rgb) + self.spawn_boundary.clear() + for ob in self.blocks + self.distractors: + self.spawn_boundary.sample( + ob, min_distance=0.08, min_rotation=(0.0, 0.0, -np.pi / 4), + max_rotation=(0.0, 0.0, np.pi / 4)) + + return ['stack %s blocks in a pyramid' % color_name, + 'create a pyramid with the %s objects' % color_name, + 'make a pyramid out of %s cubes' % color_name, + 'position the %s blocks in the shape of a pyramid' % color_name, + 'use the %s blocks to build a pyramid' % color_name] + + def variation_count(self) -> int: + return len(colors) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/change_channel.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/change_channel.py new file mode 100644 index 0000000000000000000000000000000000000000..73d0f7b54358e060f29ef3fffdd607d88279f26c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/change_channel.py @@ -0,0 +1,62 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task + + +class ChangeChannel(Task): + + def init_task(self) -> None: + self._remote = Shape('tv_remote') + self.register_graspable_objects([self._remote]) + self._joint_conditions = [ + JointCondition(Joint('target_button_joint1'), 0.0015), + JointCondition(Joint('target_button_joint2'), 0.0015) + ] + self._remote_conditions = [ + DetectedCondition(Dummy('tv_remote_top'), + ProximitySensor('success0')), + DetectedCondition(Dummy('tv_remote_bottom'), + ProximitySensor('success1')) + ] + self._spawn_boundary = SpawnBoundary([Shape('spawn_boundary')]) + self._target_buttons = [ + Shape('target_button_wrap1'), + Shape('target_button_wrap2')] + self._w6 = Dummy('waypoint6') + self._w6z = self._w6.get_position()[2] + + def init_episode(self, index: int) -> List[str]: + self.register_success_conditions( + [self._joint_conditions[index]] + self._remote_conditions) + x, y, _ = self._target_buttons[index % 2].get_position() + self._w6.set_position([x, y, self._w6z]) + self._spawn_boundary.clear() + self._spawn_boundary.sample(self._remote) + + btn = ['plus', 'minus'] + chnl = ['up', 'minus'] + return [ + 'turn the channel %s' % chnl[index], + 'change the television channel %s' % chnl[index], + 'point the remote at the tv and press the %s button to turn ' + 'the channel %s' % (btn[index], chnl[index]), + 'using the tv remote, ensure it is facing the television and ' + 'press the %s button to increment the channel %s by one' + % (btn[index], chnl[index]), + 'find the %s button on the remote, rotate the remote such that' + ' it is pointed at the tv, then press the button to change ' + 'the channel %s' % (chnl[index], btn[index])] + + def variation_count(self) -> int: + return 2 + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 2), (0.0, 0.0, np.pi / 2) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/change_clock.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/change_clock.py new file mode 100644 index 0000000000000000000000000000000000000000..fc9ba87d248d0a02ee90d720f06dad65169f44a0 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/change_clock.py @@ -0,0 +1,29 @@ +from typing import List +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import Task + + +class ChangeClock(Task): + + def init_task(self) -> None: + self.register_success_conditions([ + DetectedCondition(Shape('clock_needle_minute'), + ProximitySensor('detector_minute0')) + ]) + + def init_episode(self, index: int) -> List[str]: + return [ + 'change the clock to show time 12.15', + 'adjust the time to 12.15', + 'change the clock to 12.15', + 'set the clock to 12.15', + 'turn the knob on the back of the clock until the time shows 12.15', + 'rotate the wheel on the clock to make it show 12.15', + 'make the clock say 12.15', + 'turn the knob on the back of the clock 90 degrees' + ] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_box.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_box.py new file mode 100644 index 0000000000000000000000000000000000000000..2eefa0e4d68813350d0a9d15e1ea0867ced3cdcb --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_box.py @@ -0,0 +1,24 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class CloseBox(Task): + + def init_task(self) -> None: + self.register_success_conditions([ + JointCondition(Joint('box_joint'), 2.6)]) + + def init_episode(self, index: int) -> List[str]: + return ['close box', + 'close the lid on the box', + 'shut the box', + 'shut the box lid'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_door.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_door.py new file mode 100644 index 0000000000000000000000000000000000000000..f373bfaf9e5227c173e48751dc3b056bc362c6ce --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_door.py @@ -0,0 +1,30 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class CloseDoor(Task): + + def init_task(self) -> None: + self.register_success_conditions([ + JointCondition(Joint('door_frame_joint'), np.deg2rad(25)) + ]) + + def init_episode(self, index: int) -> List[str]: + return ['close the door', + 'shut the door', + 'grip the handle and pull the door shut', + 'use the handle to move the door closed'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 4.], [0, 0, np.pi / 4.] + + def boundary_root(self) -> Object: + return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_drawer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..2a43526f8fff8bfc9cd8fc65c1ee3e1cdffe26da --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_drawer.py @@ -0,0 +1,36 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class CloseDrawer(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint0 = Dummy('waypoint0') + + def init_episode(self, index: int) -> List[str]: + option = self._options[index] + self._joints[index].set_joint_position(0.1) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.06)]) + x, y, z = self._waypoint0.get_position() + _, _, target_z = self._anchors[index].get_position() + self._waypoint0.set_position([x, y, target_z]) + + return ['close %s drawer' % (option,), + 'shut the %s drawer' % (option,), + 'slide the %s drawer shut' % (option,)] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_fridge.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_fridge.py new file mode 100644 index 0000000000000000000000000000000000000000..cd49b9f8f20f9ecac3cde066206a2442338ce570 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_fridge.py @@ -0,0 +1,32 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class CloseFridge(Task): + + def init_task(self) -> None: + top_joint = Joint('top_joint') + self.register_success_conditions( + [JointCondition(top_joint, np.deg2rad(30))]) + + def init_episode(self, index: int) -> List[str]: + return ['close fridge', + 'close the fridge', + 'shut the fridge', + 'close the fridge door', + 'swing the fridge door shut'] + + def variation_count(self) -> int: + return 1 + + def boundary_root(self) -> Object: + return Shape('fridge_root') + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 4), (0.0, 0.0, np.pi / 4) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_grill.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_grill.py new file mode 100644 index 0000000000000000000000000000000000000000..60c2c24e4560ec2f2b685e926fcfeeae8ea9b861 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_grill.py @@ -0,0 +1,30 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class CloseGrill(Task): + + def init_task(self) -> None: + self.register_success_conditions([ + JointCondition(Joint('lid_joint'), np.deg2rad(50))]) + + def init_episode(self, index: int) -> List[str]: + return ['close the grill', + 'grasp the handle and lower the grill cover to close it', + 'close the bbq', + 'close the barbecue'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 2), (0.0, 0.0, np.pi / 2) + + def boundary_root(self) -> Object: + return Shape('grill_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_jar.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_jar.py new file mode 100644 index 0000000000000000000000000000000000000000..0f4aa7ebd905c0c79851aa41c9e14d693a3e9ccb --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_jar.py @@ -0,0 +1,63 @@ +from typing import List, Tuple +from rlbench.backend.task import Task +from typing import List +from rlbench.backend.task import Task +from rlbench.const import colors +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.dummy import Dummy + +class CloseJar(Task): + + def init_task(self) -> None: + self.lid = Shape('jar_lid0') + self.jars = [Shape('jar%d' % i) for i in range(2)] + self.register_graspable_objects([self.lid]) + self.boundary = Shape('spawn_boundary') + self.conditions = [NothingGrasped(self.robot.gripper)] + + def init_episode(self, index: int) -> List[str]: + b = SpawnBoundary([self.boundary]) + for obj in self.jars: + b.sample(obj, min_distance=0.01) + success = ProximitySensor('success') + success.set_position([0.0, 0.0, 0.05], relative_to=self.jars[index % 2], + reset_dynamics=False) + w3 = Dummy('waypoint3') + w3.set_orientation([-np.pi, 0, -np.pi], reset_dynamics=False) + w3.set_position([0.0, 0.0, 0.125], relative_to=self.jars[index % 2], + reset_dynamics=False) + target_color_name, target_color_rgb = colors[index] + color_choice = np.random.choice( + list(range(index)) + list( + range(index + 1, len(colors))), + size=1, replace=False)[0] + _, distractor_color_rgb = colors[color_choice] + self.jars[index % 2].set_color(target_color_rgb) + other_index = {0: 1, 1: 0} + self.jars[other_index[index % 2]].set_color(distractor_color_rgb) + self.conditions += [DetectedCondition(self.lid, success)] + self.register_success_conditions(self.conditions) + return ['close the %s jar' % target_color_name, + 'screw on the %s jar lid' % target_color_name, + 'grasping the lid, lift it from the table and use it to seal ' + 'the %s jar' % target_color_name, + 'pick up the lid from the table and put it on the %s jar' + % target_color_name] + + def variation_count(self) -> int: + return len(colors) + + def cleanup(self) -> None: + self.conditions = [NothingGrasped(self.robot.gripper)] + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + # This is here to stop the issue of gripper rotation joint reaching its + # limit and not being able to go through the full range of rotation to + # unscrew, leading to a weird jitery and tilted cap while unscrewing. + # Issue occured rarely so is only minor + return (0.0, 0.0, -0.6*np.pi), (0.0, 0.0, +0.6*np.pi) \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_laptop_lid.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_laptop_lid.py new file mode 100644 index 0000000000000000000000000000000000000000..92ba4b1b62b49405dfde2b766a699891ca0ea2d4 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_laptop_lid.py @@ -0,0 +1,18 @@ +from typing import List +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class CloseLaptopLid(Task): + + def init_task(self) -> None: + self.register_success_conditions([JointCondition(Joint('joint'), 1.79)]) + + def init_episode(self, index: int) -> List[str]: + return ['close laptop lid', + 'close the laptop', + 'shut the laptop lid'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_microwave.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_microwave.py new file mode 100644 index 0000000000000000000000000000000000000000..f34b52a084da6afcb3d260601d73a094e71ba04d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/close_microwave.py @@ -0,0 +1,29 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class CloseMicrowave(Task): + + def init_task(self) -> None: + self.register_success_conditions([JointCondition( + Joint('microwave_door_joint'), np.deg2rad(40))]) + + def init_episode(self, index: int) -> List[str]: + return ['close microwave', + 'shut the microwave', + 'close the microwave door', + 'push the microwave door shut'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8.], [0, 0, np.pi / 8.] + + def boundary_root(self) -> Object: + return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/empty_container.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/empty_container.py new file mode 100644 index 0000000000000000000000000000000000000000..d449be18d9e6620fb3bec3be276d0d7a0f8c4fe9 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/empty_container.py @@ -0,0 +1,108 @@ +""" +Procedural objects supplied from: +https://sites.google.com/site/brainrobotdata/home/models +""" + +from typing import List + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape + +from rlbench.backend.conditions import ConditionSet, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task +from rlbench.backend.task_utils import sample_procedural_objects +from rlbench.const import colors + + +class EmptyContainer(Task): + + def init_task(self) -> None: + self.large_container = Shape('large_container') + self.target_container0 = Shape('small_container0') + self.target_container1 = Shape('small_container1') + self.success_detector0 = ProximitySensor('success0') + self.success_detector1 = ProximitySensor('success1') + self.target_waypoint = Dummy('waypoint3') + self.spawn_boundary = SpawnBoundary([Shape('spawn_boundary')]) + self.register_waypoint_ability_start(1, self._move_above_object) + self.register_waypoints_should_repeat(self._repeat) + self.bin_objects = [] + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + self.bin_objects = sample_procedural_objects(self.get_base(), 3) + self.bin_objects_not_done = list(self.bin_objects) + self.register_graspable_objects(self.bin_objects) + self.spawn_boundary.clear() + for ob in self.bin_objects: + ob.set_position( + [0.0, 0.0, 0.2], relative_to=self.large_container, + reset_dynamics=False) + self.spawn_boundary.sample( + ob, ignore_collisions=True, min_distance=0.05) + target_pos = [-5.9605e-8, -2.5005e-1, +1.7e-1] + conditions = [] + target_color_name, target_color_rgb = colors[index] + color_choice = np.random.choice( + list(range(index)) + list( + range(index + 1, len(colors))), + size=1, replace=False)[0] + _, distractor_color_rgb = colors[color_choice] + if index % 2 == 0: + self.target_container0.set_color(target_color_rgb) + self.target_container1.set_color(distractor_color_rgb) + for ob in self.bin_objects: + conditions.append(DetectedCondition(ob, self.success_detector0)) + else: + self.target_container1.set_color(target_color_rgb) + self.target_container0.set_color(distractor_color_rgb) + for ob in self.bin_objects: + conditions.append(DetectedCondition(ob, self.success_detector1)) + target_pos[1] = -target_pos[1] + self.target_waypoint.set_position( + target_pos, relative_to=self.large_container, reset_dynamics=True) + self.register_success_conditions( + [ConditionSet(conditions, simultaneously_met=True)]) + + return ['empty the container in the to %s container' + % target_color_name, + 'clear all items from the large tray and put them in the %s ' + 'tray' % target_color_name, + 'move all objects from the large container and drop them into ' + 'the smaller %s one' % target_color_name, + 'remove whatever you find in the big box in the middle and ' + 'leave them in the %s one' % target_color_name, + 'grasp and move all objects into the %s container' + % target_color_name] + + def variation_count(self) -> int: + return len(colors) + + def cleanup(self) -> None: + [ob.remove() for ob in self.bin_objects if ob.still_exists()] + self.bin_objects = [] + + def step(self) -> None: + for ob in self.bin_objects_not_done: + if self._variation_index % 2 == 0: + if self.success_detector0.is_detected(ob): + self.bin_objects_not_done.remove(ob) + else: + if self.success_detector1.is_detected(ob): + self.bin_objects_not_done.remove(ob) + + def _move_above_object(self, waypoint): + if len(self.bin_objects_not_done) <= 0: + raise RuntimeError('Should not be here.') + bin_obj = self.bin_objects_not_done[0] + way_obj = waypoint.get_waypoint_object() + way_obj.set_position(bin_obj.get_position()) + x, y, _ = way_obj.get_orientation() + _, _, z = bin_obj.get_orientation(relative_to=way_obj) + way_obj.set_orientation([x, y, z]) + + def _repeat(self): + return len(self.bin_objects_not_done) > 0 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/empty_dishwasher.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/empty_dishwasher.py new file mode 100644 index 0000000000000000000000000000000000000000..0532b44939de0edad934180b4e4c65ae2cfda40c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/empty_dishwasher.py @@ -0,0 +1,30 @@ +from typing import List, Tuple +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from pyrep.objects.object import Object +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition + + +class EmptyDishwasher(Task): + + def init_task(self) -> None: + success_detector = ProximitySensor('success') + plate = Shape('dishwasher_plate') + self.register_graspable_objects([plate]) + self.register_success_conditions( + [DetectedCondition(plate, success_detector, negated=True)]) + + def init_episode(self, index: int) -> List[str]: + return ['empty the dishwasher', 'take dishes out of dishwasher', + 'open the dishwasher door, slide the rack out and remove the ' + 'dishes'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 2.], [0, 0, 3.14 / 2.] + + def boundary_root(self) -> Object: + return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/get_ice_from_fridge.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/get_ice_from_fridge.py new file mode 100644 index 0000000000000000000000000000000000000000..d26d00bfbe8ead06d62964d5a04185b9e8481060 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/get_ice_from_fridge.py @@ -0,0 +1,38 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape + +from rlbench.backend.conditions import DetectedCondition, JointCondition +from rlbench.backend.task import Task + + +class GetIceFromFridge(Task): + + def init_task(self) -> None: + cup = Shape('cup') + self.register_graspable_objects([cup]) + self.register_success_conditions([ + JointCondition(Joint('joint'), np.deg2rad(1)), + DetectedCondition(cup, ProximitySensor('success')) + ]) + + def init_episode(self, index: int) -> List[str]: + return ['get ice from fridge', + 'use the cup to get ice from the fridge', + 'pick up the cup and fill it with ice from the fridge', + 'push the cup up against the tongue of the ice dispenser to ' + 'retrieve ice from the fridge'] + + def variation_count(self) -> int: + return 1 + + def boundary_root(self) -> Object: + return Shape('fridge_root') + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi/4), (0.0, 0.0, np.pi/4) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hang_frame_on_hanger.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hang_frame_on_hanger.py new file mode 100644 index 0000000000000000000000000000000000000000..5df38807b7ec0bb25a42ba3a1eb71d5ff9890725 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hang_frame_on_hanger.py @@ -0,0 +1,29 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class HangFrameOnHanger(Task): + + def init_task(self) -> None: + success_sensor = ProximitySensor('success') + frame = Shape('frame') + frame_detector = Shape('frame_detector') + self.register_graspable_objects([frame]) + self.register_success_conditions( + [DetectedCondition(frame_detector, success_sensor), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['hang frame on hanger', + 'hang the picture up', + 'hang the picture frame up on the wall', + 'place the frame on the hanger'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, 0.0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hit_ball_with_queue.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hit_ball_with_queue.py new file mode 100644 index 0000000000000000000000000000000000000000..932d7b369ea469e3cff1217f349092190b9b0dc2 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hit_ball_with_queue.py @@ -0,0 +1,29 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, GraspedCondition, \ + ConditionSet + + +class HitBallWithQueue(Task): + + def init_task(self) -> None: + queue = Shape('queue') + success_sensor = ProximitySensor('success') + ball = Shape('ball') + self.register_graspable_objects([queue]) + + cond_set = ConditionSet([ + GraspedCondition(self.robot.gripper, queue), + DetectedCondition(ball, success_sensor) + ], order_matters=True) + self.register_success_conditions([cond_set]) + + def init_episode(self, index: int) -> List[str]: + return ['hit ball with queue in to the goal', + 'pot the ball in the goal', + 'pick up the que and use it to pot the ball into the goal'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hockey.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hockey.py new file mode 100644 index 0000000000000000000000000000000000000000..21f2de9bdc7f3f4c0c82ec976e9be045fd00d7d5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/hockey.py @@ -0,0 +1,29 @@ +from typing import List +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, GraspedCondition +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor + + +class Hockey(Task): + + def init_task(self) -> None: + stick = Shape('hockey_stick') + self.register_success_conditions([ + DetectedCondition(Shape('hockey_ball'), + ProximitySensor('success')), + GraspedCondition(self.robot.gripper, stick)]) + self.register_graspable_objects([stick]) + + def init_episode(self, index: int) -> List[str]: + return ['hit the ball into the net', + 'use the stick to push the hockey ball into the goal', + 'pick up the hockey stick, then swing at the ball in the ' + 'direction of the net', + 'score a hockey goal', + 'grasping one end of the hockey stick, swing it such that the ' + 'other end collides with the ball such that the ball goes ' + 'into the goal'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/insert_onto_square_peg.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/insert_onto_square_peg.py new file mode 100644 index 0000000000000000000000000000000000000000..e903d35fe49ce6e42c4e0dea858c6f62abed8393 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/insert_onto_square_peg.py @@ -0,0 +1,47 @@ +from typing import List +import numpy as np +from pyrep.objects import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, ConditionSet +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task +from rlbench.const import colors + + +class InsertOntoSquarePeg(Task): + + def init_task(self) -> None: + self._square_ring = Shape('square_ring') + self._success_centre = Dummy('success_centre') + success_detectors = [ProximitySensor( + 'success_detector%d' % i) for i in range(4)] + self.register_graspable_objects([self._square_ring]) + success_condition = ConditionSet([DetectedCondition( + self._square_ring, sd) for sd in success_detectors]) + self.register_success_conditions([success_condition]) + + def init_episode(self, index: int) -> List[str]: + color_name, color_rgb = colors[index] + spokes = [Shape('pillar0'), Shape('pillar1'), Shape('pillar2')] + chosen_pillar = np.random.choice(spokes) + chosen_pillar.set_color(color_rgb) + _, _, z = self._success_centre.get_position() + x, y, _ = chosen_pillar.get_position() + self._success_centre.set_position([x, y, z]) + + color_choices = np.random.choice( + list(range(index)) + list(range(index + 1, len(colors))), + size=2, replace=False) + spokes.remove(chosen_pillar) + for spoke, i in zip(spokes, color_choices): + name, rgb = colors[i] + spoke.set_color(rgb) + b = SpawnBoundary([Shape('boundary0')]) + b.sample(self._square_ring) + return ['put the ring on the %s spoke' % color_name, + 'slide the ring onto the %s colored spoke' % color_name, + 'place the ring onto the %s spoke' % color_name] + + def variation_count(self) -> int: + return len(colors) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/insert_usb_in_computer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/insert_usb_in_computer.py new file mode 100644 index 0000000000000000000000000000000000000000..c2be8e2f88d75b61a4bc6997555ea9ddfc8c5d6c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/insert_usb_in_computer.py @@ -0,0 +1,29 @@ +from typing import List, Tuple +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + + +class InsertUsbInComputer(Task): + + def init_task(self) -> None: + success_sensor = ProximitySensor('success') + usb = Shape('usb') + usb_tip = Shape('tip') + self.register_graspable_objects([usb]) + self.register_success_conditions( + [DetectedCondition(usb_tip, success_sensor)]) + + def init_episode(self, index: int) -> List[str]: + return ['insert usb in computer', + 'pick up the usb and put it in the computer', + 'slide the usb into the usb slot', + 'insert the usb stick into the usb port'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, 0.0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lamp_off.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lamp_off.py new file mode 100644 index 0000000000000000000000000000000000000000..283707c5b54e8ab9fa73a21211ef6967f4afaf17 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lamp_off.py @@ -0,0 +1,31 @@ +from typing import List +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint + + +class LampOff(Task): + + def init_task(self) -> None: + self.bulb_glass_visual = Shape('bulb') + self.bulb_glass_visual.set_color([1, 1, 1]) + self.joint = Joint('target_button_joint') + self.condition = JointCondition(self.joint, 0.003) + + def init_episode(self, index: int) -> List[str]: + self.bulb_glass_visual.set_color([1, 1, 1]) + self.register_success_conditions([self.condition]) + return ['turn off the light', + 'press the button to turn off the lamp', + 'press the light switch', + 'turn the lamp off', + 'close the gripper and press on the button until the light ' + 'turns off'] + + def variation_count(self) -> int: + return 1 + + def step(self) -> None: + if self.condition.condition_met() == (True, True): + self.bulb_glass_visual.set_color([0, 0, 0]) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lamp_on.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lamp_on.py new file mode 100644 index 0000000000000000000000000000000000000000..50ec173b010de0984c0dcc1944634e34f4308ab7 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lamp_on.py @@ -0,0 +1,31 @@ +from typing import List +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint + + +class LampOn(Task): + + def init_task(self) -> None: + self.bulb_glass_visual = Shape('bulb') + self.bulb_glass_visual.set_color([0, 0, 0]) + self.joint = Joint('target_button_joint') + self.condition = JointCondition(self.joint, 0.003) + + def init_episode(self, index: int) -> List[str]: + self.bulb_glass_visual.set_color([0, 0, 0]) + self.register_success_conditions([self.condition]) + return ['turn on the light', + 'press the button to turn on the lamp', + 'press the light switch', + 'turn the lamp on', + 'close the gripper and press on the button until the light ' + 'turns on'] + + def variation_count(self) -> int: + return 1 + + def step(self) -> None: + if self.condition.condition_met()[0]: + self.bulb_glass_visual.set_color([1, 1, 1]) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lift_numbered_block.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lift_numbered_block.py new file mode 100644 index 0000000000000000000000000000000000000000..843f74831b54419ea5822e5809a7f51ca3837ec5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/lift_numbered_block.py @@ -0,0 +1,44 @@ +from typing import List + +from pyrep.objects import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape + +from rlbench.backend.conditions import DetectedCondition, ConditionSet, \ + GraspedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task + + +class LiftNumberedBlock(Task): + + def init_task(self) -> None: + self._blocks = [Shape('block%d' % i) for i in range(1, 4)] + self._anchor = [Dummy('anchor%d' % i) for i in range(1, 4)] + self.register_graspable_objects(self._blocks) + self._boundary = SpawnBoundary([Shape('boundary')]) + self._success_detector = ProximitySensor('success') + self._w1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + block_num = index + 1 + target_block = self._blocks[index] + self._boundary.clear() + for block in self._blocks: + self._boundary.sample(block, min_distance=0.2) + self._w1.set_pose(self._anchor[index].get_pose()) + + self.register_success_conditions([ + GraspedCondition(self.robot.gripper, target_block), + DetectedCondition(target_block, self._success_detector) + ]) + + return ['pick up the block with the number %d' % block_num, + 'grasp the %d numbered block and lift' % block_num, + 'lift the %d numbered block' % block_num] + + def variation_count(self) -> int: + return 3 + + def is_static_workspace(self) -> bool: + return True diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/light_bulb_in.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/light_bulb_in.py new file mode 100644 index 0000000000000000000000000000000000000000..10bd57f9d00e85cb189edfa7098580865890d1f6 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/light_bulb_in.py @@ -0,0 +1,68 @@ +from typing import List +from rlbench.backend.task import Task +from rlbench.const import colors +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.dummy import Dummy + + +class LightBulbIn(Task): + + def init_task(self) -> None: + self.bulbs_visual = [Shape('light_bulb%d' % i) for i in range(2)] + self.bulb_glass_visual = [Shape('bulb%d' % i) for i in range(2)] + self.holders = [Shape('bulb_holder%d' % i) for i in range(2)] + self.bulbs = [Shape('bulb_phys%d' % i) for i in range(2)] + self.conditions = [NothingGrasped(self.robot.gripper)] + self.register_graspable_objects(self.bulbs) + self.boundary = Shape('spawn_boundary') + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + b = SpawnBoundary([self.boundary]) + for holder in self.holders: + b.sample(holder, min_distance=0.01) + self.w1 = Dummy('waypoint1') + self.w1.set_position([0, 0, +10*10**(-3)], + relative_to=self.bulb_glass_visual[index % 2], + reset_dynamics=False) + target_color_name, target_color_rgb = colors[index] + color_choice = np.random.choice( + list(range(index)) + list( + range(index + 1, len(colors))), + size=1, replace=False)[0] + _, distractor_color_rgb = colors[color_choice] + self.holders[index % 2].set_color(target_color_rgb) + other_index = {0: 1, 1: 0} + self.holders[other_index[index % 2]].set_color(distractor_color_rgb) + self.register_success_conditions([DetectedCondition( + self.bulbs[index % 2], + ProximitySensor('success')), + NothingGrasped(self.robot.gripper)]) + + return ['screw in the %s light bulb' % target_color_name, + 'screw the light bulb from the %s holder into the lamp' + % target_color_name, + 'pick up the light bulb from the %s stand, lift it up to just ' + 'above the lamp, then screw it down into the lamp in a ' + 'clockwise fashion' % target_color_name, + 'put the light bulb from the %s casing into the lamp' + % target_color_name] + + def variation_count(self) -> int: + return len(colors) + + def step(self) -> None: + if DetectedCondition(self.bulbs[self._variation_index % 2], + ProximitySensor('success')).condition_met() \ + == (True, True): + self.bulb_glass_visual[self._variation_index % 2].set_color( + [1.0, 1.0, 0.0]) + + def cleanup(self) -> None: + if self.bulb_glass_visual: + [self.bulb_glass_visual[i].set_color([1.0, 1.0, 1.0]) + for i in range(2)] \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/light_bulb_out.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/light_bulb_out.py new file mode 100644 index 0000000000000000000000000000000000000000..ef889263691273f303806ce17d347e760fbe0cfa --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/light_bulb_out.py @@ -0,0 +1,71 @@ +from typing import List +from rlbench.backend.task import Task +from rlbench.const import colors +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.dummy import Dummy + + +class LightBulbOut(Task): + + def init_task(self) -> None: + self.bulb_visual = Shape('light_bulb') + self.bulb_glass_visual = Shape('bulb') + self.holders = [Shape('bulb_holder%d' % i) for i in range(2)] + self.bulb = Shape('bulb_phys') + self.conditions = [NothingGrasped(self.robot.gripper)] + self.boundary = Shape('spawn_boundary') + self.register_graspable_objects([self.bulb]) + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + b = SpawnBoundary([self.boundary]) + for holder in self.holders: + b.sample(holder, min_distance=0.01) + ProximitySensor('success').set_position([0, 0, 0], + relative_to=self.holders[ + index % 2], + reset_dynamics=False) + w1 = Dummy('waypoint1') + w1.set_orientation([-np.pi, 0, -np.pi], reset_dynamics=False) + w4 = Dummy('waypoint4') + w4.set_orientation([-np.pi, 0, +3.735*10**(-1)], reset_dynamics=False) + target_color_name, target_color_rgb = colors[index] + color_choice = np.random.choice( + list(range(index)) + list( + range(index + 1, len(colors))), + size=1, replace=False)[0] + _, distractor_color_rgb = colors[color_choice] + self.holders[index % 2].set_color(target_color_rgb) + other_index = {0: 1, 1: 0} + self.holders[other_index[index % 2]].set_color(distractor_color_rgb) + self.register_success_conditions([DetectedCondition( + self.bulb, + ProximitySensor('success')), + NothingGrasped(self.robot.gripper)]) + return ['put the bulb in the %s holder' % target_color_name, + 'screw the bulb out and leave it in the %s stand' % + target_color_name, + 'remove the bulb from the lamp and put it in the %s stand' % + target_color_name, + 'take the light bulb out of the lamp and place it in the %s ' + 'holder' % target_color_name, + 'grasp the light bulb, twist it anti clockwise until it is no ' + 'longer attached to the lamp, and drop it into the %s stand' + % target_color_name] + + def variation_count(self) -> int: + return len(colors) + + def step(self) -> None: + if DetectedCondition(self.bulb, ProximitySensor('lamp_detector'), + negated=True).condition_met() \ + == (True, True): + self.bulb_glass_visual.set_color([1.0, 1.0, 1.0]) + + def cleanup(self) -> None: + if self.bulb_glass_visual: + self.bulb_glass_visual.set_color([1.0, 1.0, 0.0]) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/meat_off_grill.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/meat_off_grill.py new file mode 100644 index 0000000000000000000000000000000000000000..fdf4c2c7a6467e916d82f44a942af15ab5c4461d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/meat_off_grill.py @@ -0,0 +1,40 @@ +from typing import List +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.task import Task + +MEAT = ['chicken', 'steak'] + + +class MeatOffGrill(Task): + + def init_task(self) -> None: + self._steak = Shape('steak') + self._chicken = Shape('chicken') + self._success_sensor = ProximitySensor('success') + self.register_graspable_objects([self._chicken, self._steak]) + self._w1 = Dummy('waypoint1') + self._w1z= self._w1.get_position()[2] + + def init_episode(self, index: int) -> List[str]: + conditions = [NothingGrasped(self.robot.gripper)] + if index == 0: + x, y, _ = self._chicken.get_position() + self._w1.set_position([x, y, self._w1z]) + conditions.append( + DetectedCondition(self._chicken, self._success_sensor)) + else: + x, y, _ = self._steak.get_position() + self._w1.set_position([x, y, self._w1z]) + conditions.append( + DetectedCondition(self._steak, self._success_sensor)) + self.register_success_conditions(conditions) + return ['take the %s off the grill' % MEAT[index], + 'pick up the %s and place it next to the grill' % MEAT[index], + 'remove the %s from the grill and set it down to the side' + % MEAT[index]] + + def variation_count(self) -> int: + return 2 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/meat_on_grill.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/meat_on_grill.py new file mode 100644 index 0000000000000000000000000000000000000000..6d49b97929478c8c8bb0437b4cb9d58c742f1ba3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/meat_on_grill.py @@ -0,0 +1,39 @@ +from typing import List +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.task import Task + +MEAT = ['chicken', 'steak'] + + +class MeatOnGrill(Task): + + def init_task(self) -> None: + self._steak = Shape('steak') + self._chicken = Shape('chicken') + self._success_sensor = ProximitySensor('success') + self.register_graspable_objects([self._chicken, self._steak]) + self._w1 = Dummy('waypoint1') + self._w1z = self._w1.get_position()[2] + + def init_episode(self, index: int) -> List[str]: + conditions = [NothingGrasped(self.robot.gripper)] + if index == 0: + x, y, _ = self._chicken.get_position() + self._w1.set_position([x, y, self._w1z]) + conditions.append( + DetectedCondition(self._chicken, self._success_sensor)) + else: + x, y, _ = self._steak.get_position() + self._w1.set_position([x, y, self._w1z]) + conditions.append( + DetectedCondition(self._steak, self._success_sensor)) + self.register_success_conditions(conditions) + return ['put the %s on the grill' % MEAT[index], + 'pick up the %s and place it on the grill' % MEAT[index], + 'grill the %s' % MEAT[index]] + + def variation_count(self) -> int: + return 2 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/move_hanger.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/move_hanger.py new file mode 100644 index 0000000000000000000000000000000000000000..48a75ae748dc27df0e82460c64eefefa90186aaa --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/move_hanger.py @@ -0,0 +1,34 @@ +from typing import List +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +import numpy as np + + +class MoveHanger(Task): + + def init_task(self) -> None: + self.hanger = Shape('clothes_hanger0') + self.initx, self.inity, self.initz = self.hanger.get_position() + self.register_graspable_objects([self.hanger]) + success_detector = ProximitySensor('success_detector') + hanger_visual = Shape('clothes_hanger_visual0') + self.register_success_conditions([ + DetectedCondition(hanger_visual, success_detector), + NothingGrasped(self.robot.gripper) + ]) + + def init_episode(self, index: int) -> List[str]: + self.hanger.set_position( + [self.initx + np.random.uniform(-0.1, 0.3), self.inity, self.initz]) + return ['move hanger onto the other rack' + 'move the hanger from one rack to the other', + 'put the hanger on the other rack', + 'pick up the hanger and place it on the other rack'] + + def variation_count(self) -> int: + return 1 + + def is_static_workspace(self) -> bool: + return True diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_box.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_box.py new file mode 100644 index 0000000000000000000000000000000000000000..7637a0b1a32e046cb26f4661ddb35bf93d2e8f4e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_box.py @@ -0,0 +1,24 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenBox(Task): + + def init_task(self): + self.register_success_conditions([ + JointCondition(Joint('box_joint'), np.pi / 2)]) + + def init_episode(self, index: int) -> List[str]: + return ['open box', + 'open the box lid', + 'open the box', + 'grasp the lid and open the box'] + + def variation_count(self): + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_door.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_door.py new file mode 100644 index 0000000000000000000000000000000000000000..0489b05b68baae476f97df552c7ef0857bc4adf8 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_door.py @@ -0,0 +1,39 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenDoor(Task): + + def init_task(self) -> None: + self._door_joint = Joint('door_frame_joint') + self.register_success_conditions([ + JointCondition(self._door_joint, np.deg2rad(25))]) + self._door_unlock_cond = JointCondition( + Joint('door_handle_joint'), np.deg2rad(25)) + + def init_episode(self, index: int) -> List[str]: + self._door_unlocked = False + self._door_joint.set_motor_locked_at_zero_velocity(True) + return ['open the door', + 'grip the handle and push the door open', + 'use the handle to open the door'] + + def variation_count(self) -> int: + return 1 + + def step(self) -> None: + if not self._door_unlocked: + self._door_unlocked = self._door_unlock_cond.condition_met()[0] + if self._door_unlocked: + self._door_joint.set_motor_locked_at_zero_velocity(False) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 4.], [0, 0, np.pi / 4.] + + def boundary_root(self) -> Object: + return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..da2b87e24b81088a7c55593633648d6d0324e3b5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer.py @@ -0,0 +1,33 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenDrawer(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + return ['open the %s drawer' % option, + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % option] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_color_frame.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_color_frame.py new file mode 100644 index 0000000000000000000000000000000000000000..f1cc320b667ce9ddd5da3862d5711959ee4e8a9d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_color_frame.py @@ -0,0 +1,49 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task +from rlbench.const import colors + + +class OpenDrawerTestColorFrame(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + color_idx = np.random.randint(len(colors)) + color_name, color_rgb = colors[color_idx] + + drawer_frame = Shape('drawer_frame') + drawer_top = Shape('drawer_top') + drawer_middle = Shape('drawer_middle') + drawer_bottom = Shape('drawer_bottom') + + drawer_frame.set_color(color_rgb) + # drawer_top.set_color(color_rgb) + # drawer_middle.set_color(color_rgb) + # drawer_bottom.set_color(color_rgb) + + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + + return ['open the %s drawer' % (option), + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % (option)] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_color_full.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_color_full.py new file mode 100644 index 0000000000000000000000000000000000000000..096347a345a912d766aaca74863b6aa4e1f326d3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_color_full.py @@ -0,0 +1,49 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task +from rlbench.const import colors + + +class OpenDrawerTestColorFull(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + color_idx = np.random.randint(len(colors)) + color_name, color_rgb = colors[color_idx] + + drawer_frame = Shape('drawer_frame') + drawer_top = Shape('drawer_top') + drawer_middle = Shape('drawer_middle') + drawer_bottom = Shape('drawer_bottom') + + drawer_frame.set_color(color_rgb) + drawer_top.set_color(color_rgb) + drawer_middle.set_color(color_rgb) + drawer_bottom.set_color(color_rgb) + + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + + return ['open the %s drawer' % (option), + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % (option)] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_large_handles.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_large_handles.py new file mode 100644 index 0000000000000000000000000000000000000000..260d1a9a9ad4954e37cb6a791a5770dbc8122daa --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_large_handles.py @@ -0,0 +1,33 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenDrawerTestShapeLargeHandles(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + return ['open the %s drawer' % option, + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % option] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_small_body.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_small_body.py new file mode 100644 index 0000000000000000000000000000000000000000..cf183f2f205e19bf4e320b86d982bde3b6fb9ccb --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_small_body.py @@ -0,0 +1,33 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenDrawerTestShapeSmallBody(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + return ['open the %s drawer' % option, + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % option] + + def variation_count(self) -> int: + return 2 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_square_handles.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_square_handles.py new file mode 100644 index 0000000000000000000000000000000000000000..fed371c263928d8c95ef9598a5f06e155b4d05c7 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_shape_square_handles.py @@ -0,0 +1,33 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenDrawerTestShapeSquareHandles(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + return ['open the %s drawer' % option, + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % option] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_texture_frame.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_texture_frame.py new file mode 100644 index 0000000000000000000000000000000000000000..43baf3d6718d11ea9eefba563da5aa9a58fed318 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_texture_frame.py @@ -0,0 +1,73 @@ +import numpy as np +from os import path + +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.const import TextureMappingMode +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task +from rlbench.const import colors + +ASSET_DIR = path.join(path.dirname(path.abspath(__file__)), '../../', 'tests', 'unit', 'assets', 'textures') +NUM_TEXTURES = 10 + +TEX_KWARGS = { + 'mapping_mode': TextureMappingMode.PLANE, + 'repeat_along_u': True, + 'repeat_along_v': True, +} + + +class OpenDrawerTestTextureFrame(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + texture_idx = np.random.randint(NUM_TEXTURES) + texture_file = path.join(ASSET_DIR, '%d.png' % texture_idx) + text_ob, texture = self.pyrep.create_texture(texture_file) + + drawer = Shape('drawer') + drawer0 = Shape('drawer0') + drawer1 = Shape('drawer1') + drawer2 = Shape('drawer2') + drawer_frame = Shape('drawer_frame') + + drawer.set_texture(texture, **TEX_KWARGS) + drawer0.set_texture(texture, **TEX_KWARGS) + drawer1.set_texture(texture, **TEX_KWARGS) + drawer2.set_texture(texture, **TEX_KWARGS) + drawer_frame.set_texture(texture, **TEX_KWARGS) + + # ungrouped = drawer_frame.ungroup() + # for o in ungrouped: + # o.set_texture(texture, **TEX_KWARGS) + # self.pyrep.group_objects(ungrouped) + + # drawer_frame.set_texture(texture, **TEX_KWARGS) + text_ob.remove() + self.pyrep.step() + + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + + return ['open the %s drawer' % (option), + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % (option)] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_texture_full.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_texture_full.py new file mode 100644 index 0000000000000000000000000000000000000000..4e4131425d1ea83ba31d4ef0d0170a766535975a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_drawer_test_texture_full.py @@ -0,0 +1,83 @@ +import numpy as np +from os import path + +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.const import TextureMappingMode +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task +from rlbench.const import colors + +ASSET_DIR = path.join(path.dirname(path.abspath(__file__)), '../../', 'tests', 'unit', 'assets', 'textures') +NUM_TEXTURES = 10 + +TEX_KWARGS = { + 'mapping_mode': TextureMappingMode.PLANE, + 'repeat_along_u': True, + 'repeat_along_v': True, +} + + +class OpenDrawerTestTextureFull(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + + def init_episode(self, index: int) -> List[str]: + texture_idx = np.random.randint(NUM_TEXTURES) + texture_file = path.join(ASSET_DIR, '%d.png' % texture_idx) + text_ob, texture = self.pyrep.create_texture(texture_file) + + drawer = Shape('drawer') + drawer0 = Shape('drawer0') + drawer1 = Shape('drawer1') + drawer2 = Shape('drawer2') + drawer_frame = Shape('drawer_frame') + + drawer_top = Shape('drawer_top') + drawer_middle = Shape('drawer_middle') + drawer_bottom = Shape('drawer_bottom') + + drawer.set_texture(texture, **TEX_KWARGS) + drawer0.set_texture(texture, **TEX_KWARGS) + drawer1.set_texture(texture, **TEX_KWARGS) + drawer2.set_texture(texture, **TEX_KWARGS) + drawer_frame.set_texture(texture, **TEX_KWARGS) + + for objs in [drawer_top, drawer_middle, drawer_bottom]: + ungrouped = objs.ungroup() + for o in ungrouped: + o.set_texture(texture, **TEX_KWARGS) + self.pyrep.group_objects(ungrouped) + + # ungrouped = drawer_frame.ungroup() + # for o in ungrouped: + # o.set_texture(texture, **TEX_KWARGS) + # self.pyrep.group_objects(ungrouped) + + # drawer_frame.set_texture(texture, **TEX_KWARGS) + text_ob.remove() + self.pyrep.step() + + option = self._options[index] + self._waypoint1.set_position(self._anchors[index].get_position()) + self.register_success_conditions( + [JointCondition(self._joints[index], 0.15)]) + + return ['open the %s drawer' % (option), + 'grip the %s handle and pull the %s drawer open' % ( + option, option), + 'slide the %s drawer open' % (option)] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_fridge.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_fridge.py new file mode 100644 index 0000000000000000000000000000000000000000..1bca354e0b52a3808b3c1aefd4e453e01e456aaa --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_fridge.py @@ -0,0 +1,29 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenFridge(Task): + + def init_task(self): + self.register_success_conditions( + [JointCondition(Joint('top_joint'), np.deg2rad(70))]) + + def init_episode(self, index: int) -> List[str]: + return ['open fridge', + 'grip the handle and slide the fridge door open', + 'open the fridge door'] + + def variation_count(self) -> int: + return 1 + + def boundary_root(self) -> Object: + return Shape('fridge_root') + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 4), (0.0, 0.0, np.pi / 4) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_grill.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_grill.py new file mode 100644 index 0000000000000000000000000000000000000000..f159a47bb97201c7b2380a57da87a9ca79ce5e19 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_grill.py @@ -0,0 +1,30 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects import Object +from pyrep.objects.joint import Joint +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenGrill(Task): + + def init_task(self) -> None: + self.register_success_conditions([ + JointCondition(Joint('lid_joint'), np.deg2rad(50))]) + + def init_episode(self, index: int) -> List[str]: + return ['open the grill', + 'grasp tha handle and raise the cover up to open the grill', + 'open the bbq', + 'open the barbecue'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 2), (0.0, 0.0, np.pi / 2) + + def boundary_root(self) -> Object: + return Shape('grill_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_jar.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_jar.py new file mode 100644 index 0000000000000000000000000000000000000000..3f3f1f0aac4c9f30a7079baf8d0269139ee6da5f --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_jar.py @@ -0,0 +1,62 @@ +from typing import List, Tuple +from rlbench.backend.task import Task +from typing import List +from rlbench.backend.task import Task +from rlbench.const import colors +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.dummy import Dummy + +class OpenJar(Task): + + def init_task(self) -> None: + self.lids = [Shape('jar_lid%d' % i) for i in range(2)] + self.jars = [Shape('jar%d' % i) for i in range(2)] + self.register_graspable_objects(self.lids) + self.boundary = Shape('spawn_boundary') + self.conditions = [NothingGrasped(self.robot.gripper)] + + def init_episode(self, index: int) -> List[str]: + b = SpawnBoundary([self.boundary]) + success = ProximitySensor('success') + for obj in self.jars: + b.sample(obj, min_distance=0.01) + w0 = Dummy('waypoint0') + w0.set_orientation([-np.pi, 0, -np.pi], reset_dynamics=False) + w0.set_position([0,0,0.1], relative_to=self.lids[index % 2], + reset_dynamics=False) + target_color_name, target_color_rgb = colors[index] + color_choice = np.random.choice( + list(range(index)) + list( + range(index + 1, len(colors))), + size=1, replace=False)[0] + _, distractor_color_rgb = colors[color_choice] + self.jars[index % 2].set_color(target_color_rgb) + other_index = {0: 1, 1: 0} + self.jars[other_index[index % 2]].set_color(distractor_color_rgb) + self.conditions += [DetectedCondition(self.lids[index % 2], success)] + self.register_success_conditions(self.conditions) + return ['open the %s jar' % target_color_name, + 'unscrew the %s jar' % target_color_name, + 'grasp the lid of the %s jar, unscrew it in an anti_clockwise ' + 'direction until it is removed from the jar, and leave it on ' + 'the table top' % target_color_name, + 'remove the lid from the %s jam jar and set it down on the ' + 'table' % target_color_name] + + def variation_count(self) -> int: + return len(colors) + + def cleanup(self) -> None: + self.conditions = [NothingGrasped(self.robot.gripper)] + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + # This is here to stop the issue of gripper rotation joint reaching its + # limit and not being able to go through the full range of rotation to + # unscrew, leading to a weird jitery and tilted cap while unscrewing. + # Issue occured rarely so is only minor + return (0.0, 0.0, -0.6*np.pi), (0.0, 0.0, +0.6*np.pi) \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_microwave.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_microwave.py new file mode 100644 index 0000000000000000000000000000000000000000..54dded7bab36f03aeee1b7d9cf397ad06fb1db83 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_microwave.py @@ -0,0 +1,28 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenMicrowave(Task): + + def init_task(self) -> None: + self.register_success_conditions([JointCondition( + Joint('microwave_door_joint'), np.deg2rad(80))]) + + def init_episode(self, index: int) -> List[str]: + return ['open microwave', + 'open the microwave door', + 'pull the microwave door open'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8.], [0, 0, np.pi / 8.] + + def boundary_root(self) -> Object: + return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_oven.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_oven.py new file mode 100644 index 0000000000000000000000000000000000000000..0a56b08533c253ea454274211ea7504a7d4ecc58 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_oven.py @@ -0,0 +1,29 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import Task + + +class OpenOven(Task): + + def init_task(self) -> None: + self.register_success_conditions( + [DetectedCondition(Shape('oven_door'), ProximitySensor('success')), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['open the oven', + 'open the oven door', + 'grab hold of the the handle and pull the oven door open'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 4.], [0, 0, np.pi / 4.] + + def boundary_root(self) -> Object: + return Shape('oven_boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_washing_machine.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_washing_machine.py new file mode 100644 index 0000000000000000000000000000000000000000..ad61ce73945624237c93035c2edcebcb61c8b375 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_washing_machine.py @@ -0,0 +1,32 @@ +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint + +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + +from typing import List, Tuple +from math import pi + +import numpy as np + +class OpenWashingMachine(Task): + def init_task(self) -> None: + self.joint = Joint('door_joint') + self.register_success_conditions([ + JointCondition(self.joint, np.deg2rad(40)) + ]) + + def init_episode(self, index: int) -> List[str]: + return ['open washing machine', + 'open the washing machine door', + 'pull the washing machine door open'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 4.], [0, 0, 3.14 / 4.] + + def boundary_root(self) -> Object: + return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_window.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_window.py new file mode 100644 index 0000000000000000000000000000000000000000..8cd6a2d43994a3fdf5e0b352187ce76b15c3555c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_window.py @@ -0,0 +1,40 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class OpenWindow(Task): + + def init_task(self): + self._left_unlocked_cond = JointCondition( + Joint('left_handle_joint'), np.deg2rad(80)) + self._window_joint = Joint('left_window_joint') + self.register_success_conditions([JointCondition( + self._window_joint, np.deg2rad(30))]) + + def init_episode(self, index: int) -> List[str]: + self._left_unlocked = False + self._window_joint.set_motor_locked_at_zero_velocity(True) + return ['open left window', + 'rotate the handle to unlock the left window, then open it', + 'push the left window open', + 'use the handle to open the left window'] + + def variation_count(self) -> int: + return 1 + + def step(self) -> None: + if not self._left_unlocked: + self._left_unlocked = self._left_unlocked_cond.condition_met()[0] + if self._left_unlocked: + self._window_joint.set_motor_locked_at_zero_velocity(False) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 4.], [0, 0, np.pi / 4.] + + def boundary_root(self) -> Object: + return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_wine_bottle.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_wine_bottle.py new file mode 100644 index 0000000000000000000000000000000000000000..12746f916d7d6e34c6e1e6c01382cb931bca1314 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/open_wine_bottle.py @@ -0,0 +1,41 @@ +from typing import List +import numpy as np +from pyrep.objects.force_sensor import ForceSensor +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.conditions import JointCondition + + +class OpenWineBottle(Task): + + def init_task(self) -> None: + cap_detector = ProximitySensor("cap_detector") + self.joint = Joint('joint') + self.force_sensor = ForceSensor('Force_sensor') + self.cap = Shape('cap') + self.register_success_conditions( + [DetectedCondition(self.cap, cap_detector, negated=True)]) + self.cap_turned_condition = JointCondition( + self.joint, np.deg2rad(150)) + + def init_episode(self, index: int) -> List[str]: + self.cap.set_parent(self.force_sensor) + self.cap_turned = False + return ['open wine bottle', + 'screw open the wine bottle', + 'unscrew the bottle cap then remove it from the wine bottle'] + + def variation_count(self) -> int: + return 1 + + def step(self) -> None: + if not self.cap_turned: + self.cap_turned = self.cap_turned_condition.condition_met()[0] + if self.cap_turned: + self.cap.set_parent(self.joint) + + def cleanup(self): + self.cap.set_parent(self.force_sensor) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/phone_on_base.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/phone_on_base.py new file mode 100644 index 0000000000000000000000000000000000000000..ed322fa3768ca08f7b3a8cda78a1501823de1944 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/phone_on_base.py @@ -0,0 +1,29 @@ +from typing import List + +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape + +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import Task + + +class PhoneOnBase(Task): + + def init_task(self) -> None: + phone = Shape('phone') + self.register_graspable_objects([phone]) + self.register_success_conditions([ + DetectedCondition(phone, ProximitySensor('success')), + NothingGrasped(self.robot.gripper) + ]) + + def init_episode(self, index: int) -> List[str]: + return ['put the phone on the base', + 'put the phone on the stand', + 'put the hone on the hub', + 'grasp the phone and put it on the base', + 'place the phone on the base', + 'put the phone back on the base'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_and_lift.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_and_lift.py new file mode 100644 index 0000000000000000000000000000000000000000..842728f54ce7726a1529cb3edcfc2f190366d644 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_and_lift.py @@ -0,0 +1,58 @@ +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, ConditionSet, \ + GraspedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.const import colors + + +class PickAndLift(Task): + + def init_task(self) -> None: + self.target_block = Shape('pick_and_lift_target') + self.distractors = [ + Shape('stack_blocks_distractor%d' % i) + for i in range(2)] + self.register_graspable_objects([self.target_block]) + self.boundary = SpawnBoundary([Shape('pick_and_lift_boundary')]) + self.success_detector = ProximitySensor('pick_and_lift_success') + + cond_set = ConditionSet([ + GraspedCondition(self.robot.gripper, self.target_block), + DetectedCondition(self.target_block, self.success_detector) + ]) + self.register_success_conditions([cond_set]) + + def init_episode(self, index: int) -> List[str]: + + block_color_name, block_rgb = colors[index] + self.target_block.set_color(block_rgb) + + color_choices = np.random.choice( + list(range(index)) + list(range(index + 1, len(colors))), + size=2, replace=False) + for i, ob in enumerate(self.distractors): + name, rgb = colors[color_choices[int(i)]] + ob.set_color(rgb) + + self.boundary.clear() + self.boundary.sample( + self.success_detector, min_rotation=(0.0, 0.0, 0.0), + max_rotation=(0.0, 0.0, 0.0)) + for block in [self.target_block] + self.distractors: + self.boundary.sample(block, min_distance=0.1) + + return ['pick up the %s block and lift it up to the target' % + block_color_name, + 'grasp the %s block to the target' % block_color_name, + 'lift the %s block up to the target' % block_color_name] + + def variation_count(self) -> int: + return len(colors) + + def get_low_dim_state(self) -> np.ndarray: + # One of the few tasks that have a custom low_dim_state function. + return np.concatenate([self.target_block.get_position(), self.success_detector.get_position()], 0) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_and_lift_small.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_and_lift_small.py new file mode 100644 index 0000000000000000000000000000000000000000..72885ad8d589b94ba06cfe512c158024e2b80782 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_and_lift_small.py @@ -0,0 +1,47 @@ +from typing import List +from pyrep.objects import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, ConditionSet, \ + GraspedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary + +SHAPE_NAMES = ['cube', 'cylinder', 'triangular prism', 'star', 'moon'] + + +class PickAndLiftSmall(Task): + + def init_task(self) -> None: + self._shapes = [Shape(ob.replace(' ', '_')) for ob in SHAPE_NAMES] + self._grasp_points = [ + Dummy('%s_grasp_point' % ob.replace(' ', '_')) + for ob in SHAPE_NAMES] + self._w1 = Dummy('waypoint1') + + self.register_graspable_objects(self._shapes) + self.boundary = SpawnBoundary([Shape('pick_and_lift_boundary')]) + self.success_detector = ProximitySensor('pick_and_lift_success') + + def init_episode(self, index: int) -> List[str]: + shape = self._shapes[index] + self.register_success_conditions([ + GraspedCondition(self.robot.gripper, shape), + DetectedCondition(shape, self.success_detector) + ]) + self.boundary.clear() + self.boundary.sample( + self.success_detector, min_rotation=(0.0, 0.0, 0.0), + max_rotation=(0.0, 0.0, 0.0)) + for sh in self._shapes: + self.boundary.sample(sh, min_distance=0.1) + + self._w1.set_pose(self._grasp_points[index].get_pose()) + + return ['pick up the %s and lift it up to the target' % + SHAPE_NAMES[index], + 'grasp the blue %s to the target' % SHAPE_NAMES[index], + 'lift the blue %s up to the target' % SHAPE_NAMES[index]] + + def variation_count(self) -> int: + return len(SHAPE_NAMES) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_up_cup.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_up_cup.py new file mode 100644 index 0000000000000000000000000000000000000000..8933604b9086e16a83abea92a5ac6abb54b8e3d0 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pick_up_cup.py @@ -0,0 +1,47 @@ +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.const import colors +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped, GraspedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary + + +class PickUpCup(Task): + + def init_task(self) -> None: + self.cup1 = Shape('cup1') + self.cup2 = Shape('cup2') + self.cup1_visual = Shape('cup1_visual') + self.cup2_visual = Shape('cup2_visual') + self.boundary = SpawnBoundary([Shape('boundary')]) + self.success_sensor = ProximitySensor('success') + self.register_graspable_objects([self.cup1, self.cup2]) + self.register_success_conditions([ + DetectedCondition(self.cup1, self.success_sensor, negated=True), + GraspedCondition(self.robot.gripper, self.cup1), + ]) + + def init_episode(self, index: int) -> List[str]: + self.variation_index = index + target_color_name, target_rgb = colors[index] + + random_idx = np.random.choice(len(colors)) + while random_idx == index: + random_idx = np.random.choice(len(colors)) + _, other1_rgb = colors[random_idx] + + self.cup1_visual.set_color(target_rgb) + self.cup2_visual.set_color(other1_rgb) + + self.boundary.clear() + self.boundary.sample(self.cup2, min_distance=0.1) + self.boundary.sample(self.success_sensor, min_distance=0.1) + + return ['pick up the %s cup' % target_color_name, + 'grasp the %s cup and lift it' % target_color_name, + 'lift the %s cup' % target_color_name] + + def variation_count(self) -> int: + return len(colors) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_cups.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_cups.py new file mode 100644 index 0000000000000000000000000000000000000000..33597b7d2f4c0a4a455173c5bd818eb979f0602d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_cups.py @@ -0,0 +1,74 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, NothingGrasped, \ + OrConditions +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task + + +class PlaceCups(Task): + + def init_task(self) -> None: + self._cups = [Shape('mug%d' % i) for i in range(3)] + self._spokes = [Shape('place_cups_holder_spoke%d' % i) for i in + range(3)] + self._cups_boundary = Shape('mug_boundary') + self._w1 = Dummy('waypoint1') + self._w5 = Dummy('waypoint5') + success_detectors = [ + ProximitySensor('success_detector%d' % i) for i in range(3)] + self._on_peg_conditions = [OrConditions([ + DetectedCondition(self._cups[ci], success_detectors[sdi]) for sdi in + range(3)]) for ci in range(3)] + self.register_graspable_objects(self._cups) + self._initial_relative_cup = self._w1.get_pose(self._cups[0]) + self._initial_relative_spoke = self._w5.get_pose(self._spokes[0]) + + def init_episode(self, index: int) -> List[str]: + self._cups_placed = 0 + self._index = index + b = SpawnBoundary([self._cups_boundary]) + [b.sample(c, min_distance=0.10) for c in self._cups] + success_conditions = [NothingGrasped(self.robot.gripper) + ] + self._on_peg_conditions[:index + 1] + self.register_success_conditions(success_conditions) + self.register_waypoint_ability_start( + 0, self._move_above_next_target) + self.register_waypoints_should_repeat(self._repeat) + + if index == 0: + return ['place 1 cup on the cup holder', + 'pick up one cup and put it on the mug tree', + 'move 1 mug from the table to the cup holder', + 'pick up one cup and slide its handle onto a spoke on the ' + 'mug holder'] + else: + return ['place %d cups on the cup holder' % (index + 1), + 'pick up %d cups and place them on the holder' + % (index + 1), + 'move %d cups from the table to the mug tree' + % (index + 1), + 'pick up %d mugs and slide their handles onto the cup ' + 'holder spokes' % (index + 1)] + + def variation_count(self) -> int: + return 3 + + def _move_above_next_target(self, waypoint): + self._w1.set_parent(self._cups[self._cups_placed]) + self._w5.set_pose( + self._initial_relative_spoke, + relative_to=self._spokes[self._cups_placed]) + self._w1.set_pose( + self._initial_relative_cup, + relative_to=self._cups[self._cups_placed]) + self._cups_placed += 1 + + def _repeat(self): + return self._cups_placed < self._index + 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -np.pi / 2], [0.0, 0.0, np.pi / 2] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_hanger_on_rack.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_hanger_on_rack.py new file mode 100644 index 0000000000000000000000000000000000000000..a1d12763a1340c5c8748e4fbe67fa46421985bfd --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_hanger_on_rack.py @@ -0,0 +1,34 @@ +from typing import List +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + + +class PlaceHangerOnRack(Task): + + def init_task(self) -> None: + self.hanger_holder = Shape('hanger_holder') + hanger = Shape('clothes_hanger') + success_detector = ProximitySensor('success_detector0') + self.register_success_conditions([ + NothingGrasped(self.robot.gripper), + DetectedCondition(hanger, success_detector) + ]) + self.register_graspable_objects([hanger]) + self.workspace_boundary = SpawnBoundary([Shape('workspace')]) + + def init_episode(self, index: int) -> List[str]: + self.workspace_boundary.clear() + self.workspace_boundary.sample(self.hanger_holder) + return ['pick up the hanger and place in on the rack' + 'put the hanger on the rack', + 'hang the hanger up', + 'grasp the hanger and hang it up'] + + def variation_count(self) -> int: + return 1 + + def is_static_workspace(self) -> bool: + return True diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_shape_in_shape_sorter.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_shape_in_shape_sorter.py new file mode 100644 index 0000000000000000000000000000000000000000..ff331526e548929dda057d5bb675ccef835e614e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_shape_in_shape_sorter.py @@ -0,0 +1,55 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import DetectedCondition + +SHAPE_NAMES = ['cube', 'cylinder', 'triangular prism', 'star', 'moon'] + + +class PlaceShapeInShapeSorter(Task): + + def init_task(self) -> None: + self.shape_sorter = Shape('shape_sorter') + self.success_sensor = ProximitySensor('success') + self.shapes = [Shape(ob.replace(' ', '_')) for ob in SHAPE_NAMES] + self.drop_points = [ + Dummy('%s_drop_point' % ob.replace(' ', '_')) + for ob in SHAPE_NAMES] + self.grasp_points = [ + Dummy('%s_grasp_point' % ob.replace(' ', '_')) + for ob in SHAPE_NAMES] + self.waypoint1 = Dummy('waypoint1') + self.waypoint4 = Dummy('waypoint4') + self.register_graspable_objects(self.shapes) + + self.register_waypoint_ability_start(0, self._set_grasp) + self.register_waypoint_ability_start(3, self._set_drop) + self.boundary = SpawnBoundary([Shape('boundary')]) + + def init_episode(self, index) -> List[str]: + self.variation_index = index + shape = SHAPE_NAMES[index] + self.register_success_conditions( + [DetectedCondition(self.shapes[index], self.success_sensor)]) + + self.boundary.clear() + [self.boundary.sample(s, min_distance=0.05) for s in self.shapes] + + return ['put the %s in the shape sorter' % shape, + 'pick up the %s and put it in the sorter' % shape, + 'place the %s into its slot in the shape sorter' % shape, + 'slot the %s into the shape sorter' % shape] + + def variation_count(self) -> int: + return len(SHAPE_NAMES) + + def _set_grasp(self, _): + gp = self.grasp_points[self.variation_index] + self.waypoint1.set_pose(gp.get_pose()) + + def _set_drop(self, _): + dp = self.drop_points[self.variation_index] + self.waypoint4.set_pose(dp.get_pose()) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_wine_at_rack_location.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_wine_at_rack_location.py new file mode 100644 index 0000000000000000000000000000000000000000..893be3400084b026f48b9f9578d1c59732436eef --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/place_wine_at_rack_location.py @@ -0,0 +1,66 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from pyrep.objects.dummy import Dummy +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class PlaceWineAtRackLocation(Task): + + def init_task(self): + self.wine_bottle = Shape('wine_bottle') + self.register_graspable_objects([self.wine_bottle]) + + self.locations = ['middle', 'left', 'right'] + + self.register_waypoint_ability_start(3, self._move_to_rack) + + self.register_waypoint_ability_start(5, self._is_last) + self.register_waypoint_ability_start(6, self._is_last) + self.register_waypoint_ability_start(7, self._is_last) + self.register_waypoint_ability_start(8, self._is_last) + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + location = self.locations[self._variation_index] + self.register_success_conditions( + [DetectedCondition(self.wine_bottle, + ProximitySensor(f'success_{location}')), + NothingGrasped(self.robot.gripper)]) + + return ['stack the wine bottle to the %s of the rack' % (location), + 'slide the bottle onto the %s part of the rack' % (location), + 'put the wine on the %s' % (location), + 'leave the wine on the %s section of the shelf' % (location), + 'grasp the bottle and put it away on the %s' % (location)] + + def _move_to_rack(self, _): + next1, next2 = Dummy('waypoint3'), Dummy('waypoint4') + left1, left2 = Dummy('waypoint5'), Dummy('waypoint6') + right1, right2 = Dummy('waypoint7'), Dummy('waypoint8') + + if self._variation_index == 1: + next1.set_position(left1.get_position()) + next1.set_orientation(left1.get_orientation()) + + next2.set_position(left2.get_position()) + next2.set_orientation(left2.get_orientation()) + elif self._variation_index == 2: + next1.set_position(right1.get_position()) + next1.set_orientation(right1.get_orientation()) + + next2.set_position(right2.get_position()) + next2.set_orientation(right2.get_orientation()) + + + def _is_last(self, waypoint): + waypoint.skip = True + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 4.], [0, 0, np.pi / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/play_jenga.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/play_jenga.py new file mode 100644 index 0000000000000000000000000000000000000000..cb94f9508594d73623164188283ee5dc08b7a097 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/play_jenga.py @@ -0,0 +1,36 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import Task + + +class PlayJenga(Task): + + def init_task(self) -> None: + target = Shape('target_cuboid') + original_detector = ProximitySensor('original_detector') + bricks = [Shape('Cuboid%d' % i) for i in range(13)] + conds = [DetectedCondition(b, original_detector) for b in bricks] + conds.extend([ + DetectedCondition(target, original_detector, negated=True), + NothingGrasped(self.robot.gripper) + ]) + self.register_success_conditions(conds) + self.register_graspable_objects([target]) + + def init_episode(self, index: int) -> List[str]: + return ['play jenga' + 'Take the protruding block out of the jenga tower without the ' + 'tower toppling', + 'Keeping the tower from tumbling, remove the protruding ' + 'jenga block', + 'Ensuring the jenga tower remains in place, slide the ' + 'protruding block out'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/plug_charger_in_power_supply.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/plug_charger_in_power_supply.py new file mode 100644 index 0000000000000000000000000000000000000000..8b820bb3392b6cafcd819b4222c32f6b0be4b249 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/plug_charger_in_power_supply.py @@ -0,0 +1,39 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + + +class PlugChargerInPowerSupply(Task): + + def init_task(self) -> None: + sensor_right_hole = ProximitySensor('sensor1') + sensor_left_hole = ProximitySensor('sensor2') + charger = Shape('charger') + self.charger_base = Dummy('charger_base') + charger_right_stick = Shape('detector1') + charger_left_stick = Shape('detector2') + self.boundary = SpawnBoundary([Shape('charger_boundary')]) + self.register_graspable_objects([charger]) + self.register_success_conditions( + [DetectedCondition(charger_right_stick, sensor_right_hole), + DetectedCondition(charger_left_stick, sensor_left_hole), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + self.boundary.clear() + self.boundary.sample(self.charger_base) + return ['plug charger in power supply', + 'pick up the charger and plug in it', + 'plug the charger into the mains', + 'lift the charger up to the wall and plug it in', + 'plug the charger into the wall'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 4.], [0, 0, 3.14 / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pour_from_cup_to_cup.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pour_from_cup_to_cup.py new file mode 100644 index 0000000000000000000000000000000000000000..090db8c2b9a4d282d63066e96cb93ec1b52944f4 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/pour_from_cup_to_cup.py @@ -0,0 +1,88 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.const import PrimitiveShape +from rlbench.const import colors +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, ConditionSet +from rlbench.backend.spawn_boundary import SpawnBoundary + +LIQUID_BALLS = 20 + + +class PourFromCupToCup(Task): + + def init_task(self) -> None: + self.drops = [] + self.cup_target_base = Dummy('cup_target_base') + self.cup_source = Shape('cup_source') + self.cup_target = Shape('cup_target') + self.cup_source_visual = Shape('cup_source_visual') + self.cup_target_visual = Shape('cup_target_visual') + + self.distractors = [Shape('cup_distractor%d' % i) for i in range(3)] + self.distractors_vis = [Shape('cup_distractor_visual%d' % i) + for i in range(3)] + + self.success_detector = ProximitySensor('success') + self.register_graspable_objects([self.cup_source]) + + def init_episode(self, index: int) -> List[str]: + target_index = (index + 1) % self.variation_count() + source_name, source_rgb = colors[index] + target_name, target_rgb = colors[target_index] + + self.cup_source_visual.set_color(source_rgb) + self.cup_target_visual.set_color(target_rgb) + + options = (list(range(index)) + list(range(index + 1, len(colors)))) + options.remove(target_index) + + color_choices = np.random.choice(options, size=3, replace=False) + for obj, color_index in zip(self.distractors_vis, color_choices): + _, rgb = colors[color_index] + obj.set_color(rgb) + + b = SpawnBoundary([Shape('boundary')]) + b.sample(self.cup_source, min_distance=0.12) + b.sample(self.cup_target, min_distance=0.12) + [b.sample(d, min_distance=0.12) for d in self.distractors] + + # Make the waypoints always be the same orientation + self.cup_target_base.set_orientation([0.] * 3) + + self.drops = [] + conditions = [] + for i in range(LIQUID_BALLS): + drop = Shape.create(PrimitiveShape.SPHERE, mass=0.0001, + size=[0.005, 0.005, 0.005]) + drop.set_parent(self.cup_source) + drop.set_color([0.1, 0.1, 0.9]) + drop.set_position(list(np.random.normal(0, 0.0005, size=(3,))), + relative_to=self.cup_source) + self.drops.append(drop) + conditions.append(DetectedCondition(drop, self.success_detector)) + self.register_success_conditions([ConditionSet(conditions)]) + + return [ + 'pour liquid from the %s cup to the %s cup' % ( + source_name, target_name), + 'pour liquid from the %s mug to the %s mug' % ( + source_name, target_name), + 'pour the contents of the %s mug into the %s one' % ( + source_name, target_name), + 'pick up the %s cup and pour the liquid into the %s one' % ( + source_name, target_name)] + + def variation_count(self) -> int: + return len(colors) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, 0], [0, 0, 0] + + def cleanup(self) -> None: + for d in self.drops: + d.remove() + self.drops.clear() diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/press_switch.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/press_switch.py new file mode 100644 index 0000000000000000000000000000000000000000..8b58d3bcc9c7ba3101da89c5d591830615fb6c2e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/press_switch.py @@ -0,0 +1,19 @@ +from typing import List +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition + + +class PressSwitch(Task): + + def init_task(self) -> None: + switch_joint = Joint('joint') + self.register_success_conditions([JointCondition(switch_joint, 1.0)]) + + def init_episode(self, index: int) -> List[str]: + return ['press switch', + 'turn the switch on or off', + 'flick the switch'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/push_button.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/push_button.py new file mode 100644 index 0000000000000000000000000000000000000000..9ba1afa22af258d9a0ddb9aa90027873f30955a3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/push_button.py @@ -0,0 +1,61 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition,ConditionSet + +# button top plate and wrapper will be be red before task completion +# and be changed to cyan upon success of task, so colors list used to randomly vary colors of +# base block will be redefined, excluding red and green +colors = [ + ('maroon', (0.5, 0.0, 0.0)), + ('green', (0.0, 0.5, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('navy', (0.0, 0.0, 0.5)), + ('yellow', (1.0, 1.0, 0.0)), + ('cyan', (0.0, 1.0, 1.0)), + ('magenta', (1.0, 0.0, 1.0)), + ('silver', (0.75, 0.75, 0.75)), + ('gray', (0.5, 0.5, 0.5)), + ('orange', (1.0, 0.5, 0.0)), + ('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + ('teal', (0, 0.5, 0.5)), + ('azure', (0.0, 0.5, 1.0)), + ('violet', (0.5, 0.0, 1.0)), + ('rose', (1.0, 0.0, 0.5)), + ('black', (0.0, 0.0, 0.0)), + ('white', (1.0, 1.0, 1.0)), +] + + +class PushButton(Task): + + def init_task(self): + self.target_button = Shape('push_button_target') + self.target_topPlate = Shape('target_button_topPlate') + self.joint = Joint('target_button_joint') + self.target_wrap = Shape('target_button_wrap') + self.goal_condition = JointCondition(self.joint, 0.003) + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + self.target_topPlate.set_color([1.0, 0.0, 0.0]) + self.target_wrap.set_color([1.0, 0.0, 0.0]) + self.variation_index = index + button_color_name, button_rgb = colors[index] + self.target_button.set_color(button_rgb) + self.register_success_conditions( + [ConditionSet([self.goal_condition], True, False)]) + return ['push the %s button' % button_color_name, + 'push down the %s button' % button_color_name, + 'press the button with the %s base' % button_color_name, + 'press the %s button' % button_color_name] + + def variation_count(self) -> int: + return len(colors) + + def step(self) -> None: + if self.goal_condition.condition_met() == (True, True): + self.target_topPlate.set_color([0.0, 1.0, 0.0]) + self.target_wrap.set_color([0.0, 1.0, 0.0]) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/push_buttons.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/push_buttons.py new file mode 100644 index 0000000000000000000000000000000000000000..9b5ba3b8a8bb62d9d8682f112c99cb115c7fb141 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/push_buttons.py @@ -0,0 +1,167 @@ +from typing import List +import itertools +import math +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import JointCondition, ConditionSet + +MAX_TARGET_BUTTONS = 3 +MAX_VARIATIONS = 50 + +# button top plate and wrapper will be be red before task completion +# and be changed to cyan upon success of task, so colors list used to randomly vary colors of +# base block will be redefined, excluding red and green +colors = [ + ('maroon', (0.5, 0.0, 0.0)), + ('green', (0.0, 0.5, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('navy', (0.0, 0.0, 0.5)), + ('yellow', (1.0, 1.0, 0.0)), + ('cyan', (0.0, 1.0, 1.0)), + ('magenta', (1.0, 0.0, 1.0)), + ('silver', (0.75, 0.75, 0.75)), + ('gray', (0.5, 0.5, 0.5)), + ('orange', (1.0, 0.5, 0.0)), + ('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + ('teal', (0, 0.5, 0.5)), + ('azure', (0.0, 0.5, 1.0)), + ('violet', (0.5, 0.0, 1.0)), + ('rose', (1.0, 0.0, 0.5)), + ('black', (0.0, 0.0, 0.0)), + ('white', (1.0, 1.0, 1.0)), +] + +color_permutations = list(itertools.permutations(colors, 3)) + + +def print_permutations(color_permutations): + # pretty printing color_permutations for debug + print('num permutations: ', str(len(color_permutations))) + print('color_permutations:\n') + for i in range(len(color_permutations)): + print(str(color_permutations[i])) + if ((i + 1) % 16 == 0): print('') + + +class PushButtons(Task): + + def init_task(self) -> None: + self.buttons_pushed = 0 + self.color_variation_index = 0 + self.target_buttons = [Shape('push_buttons_target%d' % i) + for i in range(3)] + self.target_topPlates = [Shape('target_button_topPlate%d' % i) + for i in range(3)] + self.target_joints = [Joint('target_button_joint%d' % i) + for i in range(3)] + self.target_wraps = [Shape('target_button_wrap%d' % i) + for i in range(3)] + self.boundaries = Shape('push_buttons_boundary') + # goal_conditions merely state joint conditions for push action for + # each button regardless of whether the task involves pushing it + self.goal_conditions = [JointCondition(self.target_joints[n], 0.001) + for n in range(3)] + + self.register_waypoint_ability_start(0, self._move_above_next_target) + self.register_waypoints_should_repeat(self._repeat) + + def init_episode(self, index: int) -> List[str]: + for tp in self.target_topPlates: + tp.set_color([1.0, 0.0, 0.0]) + for w in self.target_wraps: + w.set_color([1.0, 0.0, 0.0]) + # For each color permutation, we want to have 1, 2 or 3 buttons pushed + color_index = int(index / MAX_TARGET_BUTTONS) + self.buttons_to_push = 1 + index % MAX_TARGET_BUTTONS + button_colors = color_permutations[color_index] + + self.color_names = [] + self.color_rgbs = [] + self.chosen_colors = [] + i = 0 + for b in self.target_buttons: + color_name, color_rgb = button_colors[i] + self.color_names.append(color_name) + self.color_rgbs.append(color_rgb) + self.chosen_colors.append((color_name, color_rgb)) + b.set_color(color_rgb) + i += 1 + + # for task success, all button to push must have green color RGB + self.success_conditions = [] + for i in range(self.buttons_to_push): + self.success_conditions.append(self.goal_conditions[i]) + + self.register_success_conditions( + [ConditionSet(self.success_conditions, True, False)]) + + rtn0 = 'push the %s button' % self.color_names[0] + rtn1 = 'press the %s button' % self.color_names[0] + rtn2 = 'push down the button with the %s base' % self.color_names[0] + for i in range(self.buttons_to_push): + if i == 0: + continue + else: + rtn0 += ', then push the %s button' % self.color_names[i] + rtn1 += ', then press the %s button' % self.color_names[i] + rtn2 += ', then the %s one' % self.color_names[i] + + b = SpawnBoundary([self.boundaries]) + for button in self.target_buttons: + b.sample(button, min_distance=0.1) + + num_non_targets = 3 - self.buttons_to_push + spare_colors = list(set(colors) + - set( + [self.chosen_colors[i] for i in range(self.buttons_to_push)])) + + spare_color_rgbs = [] + for i in range(len(spare_colors)): + _, rgb = spare_colors[i] + spare_color_rgbs.append(rgb) + + color_choice_indexes = np.random.choice(range(len(spare_colors)), + size=num_non_targets, + replace=False) + non_target_index = 0 + for i, button in enumerate(self.target_buttons): + if i in range(self.buttons_to_push): + pass + else: + _, rgb = spare_colors[color_choice_indexes[non_target_index]] + button.set_color(rgb) + non_target_index += 1 + + return [rtn0, rtn1, rtn2] + + def variation_count(self) -> int: + return np.minimum( + len(color_permutations) * MAX_TARGET_BUTTONS, MAX_VARIATIONS) + + def step(self) -> None: + for i in range(len(self.target_buttons)): + if self.goal_conditions[i].condition_met() == (True, True): + self.target_topPlates[i].set_color([0.0, 1.0, 0.0]) + self.target_wraps[i].set_color([0.0, 1.0, 0.0]) + + def cleanup(self) -> None: + self.buttons_pushed = 0 + + def _move_above_next_target(self, waypoint): + if self.buttons_pushed >= self.buttons_to_push: + print('buttons_pushed:', self.buttons_pushed, 'buttons_to_push:', + self.buttons_to_push) + raise RuntimeError('Should not be here.') + w0 = Dummy('waypoint0') + x, y, z = self.target_buttons[self.buttons_pushed].get_position() + w0.set_position([x, y, z + 0.083]) + w0.set_orientation([math.pi, 0, math.pi]) + + def _repeat(self): + self.buttons_pushed += 1 + return self.buttons_pushed < self.buttons_to_push diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_all_groceries_in_cupboard.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_all_groceries_in_cupboard.py new file mode 100644 index 0000000000000000000000000000000000000000..5e96e6e1b90696495d8528e40f741532b5054ca8 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_all_groceries_in_cupboard.py @@ -0,0 +1,79 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped, \ + DetectedSeveralCondition +from rlbench.backend.spawn_boundary import SpawnBoundary + +GROCERY_NAMES = [ + 'crackers', + 'chocolate jello', + 'strawberry jello', + 'soup', + 'spam', + 'mustard', + 'sugar', +] + + +class PutAllGroceriesInCupboard(Task): + + def init_task(self) -> None: + self.groceries = [Shape(name.replace(' ', '_')) + for name in GROCERY_NAMES] + self.grasp_points = [Dummy('%s_grasp_point' % name.replace(' ', '_')) + for name in GROCERY_NAMES] + self.goals = [Dummy('goal_%s' % name.replace(' ', '_')) + for name in GROCERY_NAMES] + self.waypoint1 = Dummy('waypoint1') + self.waypoint3 = Dummy('waypoint3') + self.register_graspable_objects(self.groceries) + self.boundary = SpawnBoundary([Shape('groceries_boundary')]) + + self.register_waypoint_ability_start(0, self._move_to_next_target) + self.register_waypoint_ability_start(3, self._move_to_drop_zone) + self.register_waypoints_should_repeat(self._repeat) + self.groceries_to_place = len(GROCERY_NAMES) + self.groceries_placed = 0 + + def init_episode(self, index: int) -> List[str]: + self.groceries_placed = 0 + self.boundary.clear() + [self.boundary.sample(g, min_distance=0.15) for g in self.groceries] + + self.register_success_conditions( + [DetectedSeveralCondition( + self.groceries[:self.groceries_to_place], + ProximitySensor('success'), self.groceries_to_place), + NothingGrasped(self.robot.gripper)]) + return ['put all of the groceries in the cupboard', + 'pick up all of the groceries and place them in the cupboard', + 'move the groceries to the shelves', + 'put the groceries on the table into the cupboard', + 'put away the groceries in the cupboard'] + + def variation_count(self) -> int: + return 1 + + def boundary_root(self) -> Object: + return Shape('boundary_root') + + def is_static_workspace(self) -> bool: + return True + + def _move_to_next_target(self, _): + if self.groceries_placed >= self.groceries_to_place: + raise RuntimeError('Should not be here.') + self.waypoint1.set_pose( + self.grasp_points[self.groceries_placed].get_pose()) + + def _move_to_drop_zone(self, _): + self.waypoint3.set_pose( + self.goals[self.groceries_placed].get_pose()) + + def _repeat(self): + self.groceries_placed += 1 + return self.groceries_placed < self.groceries_to_place \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_books_at_shelf_location.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_books_at_shelf_location.py new file mode 100644 index 0000000000000000000000000000000000000000..a65a8a2251d8b27bab7fbe7f0e5ef74c91c56587 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_books_at_shelf_location.py @@ -0,0 +1,84 @@ +from typing import List, Tuple + +from pyrep.objects import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class PutBooksAtShelfLocation(Task): + + def init_task(self) -> None: + # self._success_sensor_top = ProximitySensor('success_top') + # self._success_sensor_bottom = ProximitySensor('success_bottom') + self._books = [Shape('book2'), Shape('book1'), Shape('book0')] + self._waypoints_idxs = [5, 11, -1] + self.register_graspable_objects(self._books) + + self._book2_waypoints = [Dummy('waypoint%d' % i) for i in [3, 4, 5]] + self._book1_waypoints = [Dummy('waypoint%d' % i) for i in [9, 10, 11]] + self._book0_waypoints = [Dummy('waypoint%d' % i) for i in [15, 16]] + + self._variations = { + 0: { + 'seq': ['top_left', 'top_middle', 'top_right'], + 'success': ['success_top', 'success_top', 'success_top'], + 'lang': "put all books on the top shelf" + }, + 1: { + 'seq': ['top_left', 'bottom_middle', 'top_right'], + 'success': ['success_top', 'success_bottom', 'success_top'], + 'lang': "put one book at the bottom and two books on the top" + }, + 2: { + 'seq': ['top_left', 'bottom_middle', 'bottom_right'], + 'success': ['success_top', 'success_bottom', 'success_bottom'], + 'lang': "put one book at the top and two books at the bottom" + }, + 3: { + 'seq': ['bottom_left', 'bottom_middle', 'bottom_right'], + 'success': ['success_bottom', 'success_bottom', 'success_bottom'], + 'lang': "put all books on the bottom shelf" + }, + } + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + setup = self._variations[self._variation_index] + + # success condition + success_conditions = [] + for book_idx, book in enumerate(self._books): + success_conditions.append( + DetectedCondition( + book, ProximitySensor(setup['success'][book_idx]) + ) + ) + + # waypoints + for idx, w in enumerate(self._book2_waypoints): + w.set_pose(Dummy("%s_%d" % (setup['seq'][0], idx+1)).get_pose()) + for idx, w in enumerate(self._book1_waypoints): + w.set_pose(Dummy("%s_%d" % (setup['seq'][1], idx+1)).get_pose()) + for idx, w in enumerate(self._book0_waypoints): + w.set_pose(Dummy("%s_%d" % (setup['seq'][2], idx+1)).get_pose()) + + + # language instruction + return [setup['lang']] + + # self.register_success_conditions([ + # DetectedCondition( + # b, self._success_sensor) for b in self._books[:index+1] + # ]) + # self.register_stop_at_waypoint(self._waypoints_idxs[index]) + # return ['put %d books on bookshelf' % (index + 1), + # 'pick up %d books and place them on the top shelf' % (index + 1), + # 'stack %d books up on the top shelf' % (index + 1)] + + def variation_count(self) -> int: + return 4 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -3.14/2], [0.0, 0.0, 3.14/2] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_books_on_bookshelf.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_books_on_bookshelf.py new file mode 100644 index 0000000000000000000000000000000000000000..4b39d44662924a1a2a786c1bf55c49060a5e00ac --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_books_on_bookshelf.py @@ -0,0 +1,32 @@ +from typing import List, Tuple + +from pyrep.objects import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class PutBooksOnBookshelf(Task): + + def init_task(self) -> None: + self._success_sensor = ProximitySensor('success') + self._books = [Shape('book2'), Shape('book1'), Shape('book0')] + self._waypoints_idxs = [5, 11, -1] + self.register_graspable_objects(self._books) + + def init_episode(self, index: int) -> List[str]: + self.register_success_conditions([ + DetectedCondition( + b, self._success_sensor) for b in self._books[:index+1] + ]) + self.register_stop_at_waypoint(self._waypoints_idxs[index]) + return ['put %d books on bookshelf' % (index + 1), + 'pick up %d books and place them on the top shelf' % (index + 1), + 'stack %d books up on the top shelf' % (index + 1)] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -3.14/2], [0.0, 0.0, 3.14/2] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_bottle_in_fridge.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_bottle_in_fridge.py new file mode 100644 index 0000000000000000000000000000000000000000..0b68099012adecd2fad0238147a1cedcd72293bf --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_bottle_in_fridge.py @@ -0,0 +1,34 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import Task + + +class PutBottleInFridge(Task): + + def init_task(self) -> None: + bottle = Shape('bottle') + self.register_graspable_objects([bottle]) + self.register_success_conditions( + [DetectedCondition(bottle, ProximitySensor('success')), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['put bottle in fridge', + 'place the bottle inside the fridge', + 'open the fridge and put the bottle in there', + 'open the fridge door, pick up the bottle, and leave it in the ' + 'fridge'] + + def variation_count(self) -> int: + return 1 + + def boundary_root(self) -> Object: + return Shape('fridge_root') + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 4), (0.0, 0.0, np.pi / 4) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_groceries_in_cupboard.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_groceries_in_cupboard.py new file mode 100644 index 0000000000000000000000000000000000000000..7325b3fa91c07da3fe1c6faff07971b1868de05a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_groceries_in_cupboard.py @@ -0,0 +1,57 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + +GROCERY_NAMES = [ + 'crackers', + 'chocolate jello', + 'strawberry jello', + 'soup', + 'tuna', + 'spam', + 'coffee', + 'mustard', + 'sugar', +] + + +class PutGroceriesInCupboard(Task): + + def init_task(self) -> None: + self.groceries = [Shape(name.replace(' ', '_')) + for name in GROCERY_NAMES] + self.grasp_points = [Dummy('%s_grasp_point' % name.replace(' ', '_')) + for name in GROCERY_NAMES] + self.waypoint1 = Dummy('waypoint1') + self.register_graspable_objects(self.groceries) + self.boundary = SpawnBoundary([Shape('workspace')]) + + def init_episode(self, index: int) -> List[str]: + self.boundary.clear() + [self.boundary.sample(g, min_distance=0.1) for g in self.groceries] + self.waypoint1.set_pose(self.grasp_points[index].get_pose()) + self.register_success_conditions( + [DetectedCondition(self.groceries[index], + ProximitySensor('success')), + NothingGrasped(self.robot.gripper)]) + return ['put the %s in the cupboard' % GROCERY_NAMES[index], + 'pick up the %s and place it in the cupboard' + % GROCERY_NAMES[index], + 'move the %s to the bottom shelf' % GROCERY_NAMES[index], + 'put away the %s in the cupboard' % GROCERY_NAMES[index]] + + def variation_count(self) -> int: + return len(GROCERY_NAMES) + + def boundary_root(self) -> Object: + return Shape('boundary_root') + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -1.), (0.0, 0.0, 1.) + diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_item_in_drawer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_item_in_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..31e28c6d864bc7ad2b5ad43d457cc55cf6ddf109 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_item_in_drawer.py @@ -0,0 +1,40 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task + + +class PutItemInDrawer(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint2') + self._item = Shape('item') + self.register_graspable_objects([self._item]) + + def init_episode(self, index) -> List[str]: + option = self._options[index] + anchor = self._anchors[index] + self._waypoint1.set_position(anchor.get_position()) + success_sensor = ProximitySensor('success_' + option) + self.register_success_conditions( + [DetectedCondition(self._item, success_sensor)]) + return ['put the item in the %s drawer' % option, + 'put the block away in the %s drawer' % option, + 'open the %s drawer and place the block inside of it' % option, + 'leave the block in the %s drawer' % option] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_knife_in_knife_block.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_knife_in_knife_block.py new file mode 100644 index 0000000000000000000000000000000000000000..0265d621b53ba27ecd17634045aabc3f9af7b929 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_knife_in_knife_block.py @@ -0,0 +1,43 @@ +from typing import List, Tuple +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import ConditionSet, DetectedCondition, \ + NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task + + +class PutKnifeInKnifeBlock(Task): + + def init_task(self) -> None: + knife = Shape('knife') + self._knife_base = Dummy('knife_base') + self._knife_block = Shape('knife_block') + self._chopping_board = Shape('chopping_board') + self._boundary = SpawnBoundary([Shape('boundary')]) + self._knife_bound = SpawnBoundary([Shape('knife_boundary')]) + self.register_graspable_objects([knife]) + self.register_success_conditions([ + ConditionSet([ + DetectedCondition(knife, ProximitySensor('success')), + NothingGrasped(self.robot.gripper)], + order_matters=True)]) + + def init_episode(self, index: int) -> List[str]: + self._knife_bound.clear() + self._boundary.clear() + self._boundary.sample(self._knife_block) + self._boundary.sample(self._chopping_board) + self._knife_bound.sample(self._knife_base) + return ['put the knife in the knife block', + 'slide the knife into its slot in the knife block', + 'place the knife in the knife block', + 'pick up the knife and leave it in its holder', + 'move the knife from the chopping board to the holder'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, 0], [0, 0, 0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_knife_on_chopping_board.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_knife_on_chopping_board.py new file mode 100644 index 0000000000000000000000000000000000000000..d94b286fa4dac9e23fc34d96a849735fba5c4409 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_knife_on_chopping_board.py @@ -0,0 +1,33 @@ +from typing import List +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import ConditionSet, DetectedCondition, \ + NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task + + +class PutKnifeOnChoppingBoard(Task): + + def init_task(self) -> None: + knife = Shape('knife') + self._knife_block = Shape('knife_block') + self._boundary = SpawnBoundary([Shape('boundary')]) + self.register_graspable_objects([knife]) + self.register_success_conditions([ + ConditionSet([DetectedCondition(knife, ProximitySensor('success')), + NothingGrasped(self.robot.gripper)], + order_matters=True)]) + + def init_episode(self, index: int) -> List[str]: + self._boundary.clear() + self._boundary.sample(self._knife_block) + return ['put the knife on the chopping board', + 'slide the knife out of the knife block and put it down on the ' + 'chopping board', + 'place the knife on the chopping board', + 'pick up the knife and leave it on the chopping board', + 'move the knife from the holder to the chopping board'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_money_in_safe.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_money_in_safe.py new file mode 100644 index 0000000000000000000000000000000000000000..a1370ef56606aabb5adf5651e98be3ffd101b1c8 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_money_in_safe.py @@ -0,0 +1,62 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + +NUM_SHELVES_IN_SAFE = 3 + + +class PutMoneyInSafe(Task): + + def init_task(self) -> None: + self.index_dic = {0: 'bottom', 1: 'middle', 2: 'top'} + self.money = Shape('dollar_stack') + self.money_boundary = Shape('dollar_stack_boundary') + self.register_graspable_objects([self.money]) + self.success_conditions = [NothingGrasped(self.robot.gripper)] + + self.w1_rel_pos = [-2.7287 * 10 ** (-4), -2.3246 * 10 ** (-6), + +4.5627 * 10 ** (-2)] + self.w1_rel_ori = [-3.1416, 7.2824 * 10 ** (-1), -2.1265 * 10 ** (-2)] + + def init_episode(self, index: int) -> List[str]: + self.target_shelf = index + w4 = Dummy('waypoint4') + target_dummy_name = 'dummy_shelf' + str(self.target_shelf) + target_pos_dummy = Dummy(target_dummy_name) + target_pos = target_pos_dummy.get_position() + w4.set_position(target_pos, reset_dynamics=False) + + self.success_detector = ProximitySensor( + ('success_detector' + str(self.target_shelf)) + ) + + while len(self.success_conditions) > 1: + self.success_conditions.pop() + + self.success_conditions.append( + DetectedCondition(self.money, self.success_detector) + ) + self.register_success_conditions(self.success_conditions) + + b = SpawnBoundary([self.money_boundary]) + b.sample(self.money, + min_rotation=(0.00, 0.00, 0.00), + max_rotation=(0.00, 0.00, +0.5 * np.pi)) + + return ['put the money away in the safe on the %s shelf' + % self.index_dic[index], + 'leave the money on the %s shelf on the safe' + % self.index_dic[index], + 'place the stack of bank notes on the %s shelf of the safe' + % self.index_dic[index]] + + def variation_count(self) -> int: + return NUM_SHELVES_IN_SAFE + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, +0.5 * np.pi] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_plate_in_colored_dish_rack.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_plate_in_colored_dish_rack.py new file mode 100644 index 0000000000000000000000000000000000000000..ecc1bac3a3e347319c77275ade9a849f72f56daa --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_plate_in_colored_dish_rack.py @@ -0,0 +1,70 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import DetectedCondition, \ + NothingGrasped, ConditionSet +from rlbench.const import colors + + +class PutPlateInColoredDishRack(Task): + + def init_task(self) -> None: + plate = Shape('plate') + self.dish_rack = Shape('dish_rack') + self.plate_stand = Shape('plate_stand') + self.success_sensor = ProximitySensor('success') + self.success_poses = [Dummy('success_pos%d' % i) + for i in range(3)] + self.pillars = [Shape('dish_rack_pillar%d' % i) + for i in range(6)] + + self.boundary = SpawnBoundary([Shape('boundary')]) + self.register_graspable_objects([plate]) + cond_set = ConditionSet([DetectedCondition(plate, self.success_sensor), + NothingGrasped(self.robot.gripper)], + order_matters=True) + self.register_success_conditions([cond_set]) + + def init_episode(self, index: int) -> List[str]: + color_name, _ = colors[index] + + shuffled_pillar_indexes = list(range(3)) + np.random.shuffle(shuffled_pillar_indexes) + + # Color the other spokes + color_choices = [index] + list(np.random.choice( + list(range(index)) + list(range(index + 1, len(colors))), + size=2, replace=False)) + for pillar_i, color_i in zip(shuffled_pillar_indexes, color_choices): + _, rgb = colors[color_i] + self.pillars[pillar_i * 2].set_color(rgb) + self.pillars[1 + pillar_i * 2].set_color(rgb) + + # Move the success detector to the correct spot + success_pos = self.success_poses[shuffled_pillar_indexes[0]] + self.success_sensor.set_position(success_pos.get_position()) + self.success_sensor.set_orientation(success_pos.get_orientation()) + + self.boundary.clear() + self.boundary.sample( + self.plate_stand, min_rotation=(0, 0, 0.5), + max_rotation=(0, 0, 0.5)) + self.boundary.sample(self.dish_rack, min_distance=0.2) + + return ['put the plate between the %s pillars of the dish rack'# + % color_name, + 'place the plate in the the %s section of the drying rack' + % color_name, + 'pick up the plate and leave it between the %s spokes on the ' + 'dish rack' % color_name] + + + def variation_count(self) -> int: + return len(colors) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, 0], [0, 0, 0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_rubbish_in_bin.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_rubbish_in_bin.py new file mode 100644 index 0000000000000000000000000000000000000000..5cb4077b63893e6b4466513b6203e6ed07f33d2f --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_rubbish_in_bin.py @@ -0,0 +1,39 @@ +from typing import List +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition + + +class PutRubbishInBin(Task): + + def init_task(self): + success_sensor = ProximitySensor('success') + self.rubbish = Shape('rubbish') + self.register_graspable_objects([self.rubbish]) + self.register_success_conditions( + [DetectedCondition(self.rubbish, success_sensor)]) + + def init_episode(self, index: int) -> List[str]: + tomato1 = Shape('tomato1') + tomato2 = Shape('tomato2') + x1, y1, z1 = tomato2.get_position() + x2, y2, z2 = self.rubbish.get_position() + x3, y3, z3 = tomato1.get_position() + pos = np.random.randint(3) + if pos == 0: + self.rubbish.set_position([x1, y1, z2]) + tomato2.set_position([x2, y2, z1]) + elif pos == 2: + self.rubbish.set_position([x3, y3, z2]) + tomato1.set_position([x2, y2, z3]) + + return ['put rubbish in bin', + 'drop the rubbish into the bin', + 'pick up the rubbish and leave it in the trash can', + 'throw away the trash, leaving any other objects alone', + 'chuck way any rubbish on the table rubbish'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_rubbish_in_color_bin.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_rubbish_in_color_bin.py new file mode 100644 index 0000000000000000000000000000000000000000..b1bcbc62e17ecef6c03cbd073dffd3b31ab56e17 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_rubbish_in_color_bin.py @@ -0,0 +1,68 @@ +from typing import List +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from pyrep.objects.dummy import Dummy +from rlbench.backend.conditions import DetectedCondition +from rlbench.const import colors + + +class PutRubbishInColorBin(Task): + + COLORS = [colors[0], colors[3]] + + def init_task(self) -> None: + self.rubbish = Shape('rubbish') + self.register_graspable_objects([self.rubbish]) + + self.register_waypoint_ability_start(3, self._move_above_bin) + self.register_waypoint_ability_start(4, self._is_last) + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + success_sensor = ProximitySensor(f'success{self._variation_index+1}') + self.register_success_conditions( + [DetectedCondition(self.rubbish, success_sensor)]) + + self.main_bin = Shape(f'bin{self._variation_index+1}') + self.distractor_bin = Shape(f'bin{1 if self._variation_index+1 == 2 else 2}') + + color_bulb = Shape('color_bulb') + color_name, color_rgb = self.COLORS[self._variation_index] + color_bulb.set_color(color_rgb) + + x1, y1, z1 = color_bulb.get_position() + x2, y2, z2 = self.rubbish.get_position() + x3, y3, z3 = self.main_bin.get_position() + x4, y4, z4 = self.distractor_bin.get_position() + pos = np.random.randint(2) + if pos == 0: + self.main_bin.set_position([x4, y4, z4]) + self.distractor_bin.set_position([x3, y3, z3]) + + return ['put rubbish in the bin with the same color as the light', + 'drop the rubbish into the bin with the color of the light', + 'pick up the trash and leave it in the bin with the same color as the light'] + + def variation_count(self) -> int: + return 2 + + def _move_above_bin(self, _): + w3 = Dummy('waypoint3') + w4 = Dummy('waypoint4') + + if self._variation_index == 1: + w3.set_position(w4.get_position()) + w3.set_orientation(w4.get_orientation()) + + def _is_last(self, waypoint): + waypoint.skip = True + + def step(self) -> None: + # Called during each sim step. Remove this if not using. + pass + + def cleanup(self) -> None: + # Called during at the end of each episode. Remove this if not using. + pass diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_shoes_in_box.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_shoes_in_box.py new file mode 100644 index 0000000000000000000000000000000000000000..50001e56759e525a7e434ec635d9bbe724e6f65e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_shoes_in_box.py @@ -0,0 +1,29 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class PutShoesInBox(Task): + + def init_task(self): + shoe1, shoe2 = Shape('shoe1'), Shape('shoe2') + self.register_graspable_objects([shoe1, shoe2]) + success_sensor = ProximitySensor('success_in_box') + self.register_success_conditions([ + DetectedCondition(shoe1, success_sensor), + DetectedCondition(shoe2, success_sensor), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['put the shoes in the box', + 'open the box and place the shoes inside', + 'open the box lid and put the shoes inside'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_toilet_roll_on_stand.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_toilet_roll_on_stand.py new file mode 100644 index 0000000000000000000000000000000000000000..12a444a83f6976c1d3e26fc3c95b7b9bf7ed7b9e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_toilet_roll_on_stand.py @@ -0,0 +1,33 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + + +class PutToiletRollOnStand(Task): + + def init_task(self) -> None: + roll = Shape('toilet_roll') + self.boundary_roll = SpawnBoundary([Shape('boundary_roll')]) + self.boundary_stand = SpawnBoundary([Shape('boundary_stand')]) + self.roll_box = Shape('toilet_roll_box') + self.stand_base = Shape('stand_base') + success_sensor = ProximitySensor('success') + self.register_graspable_objects([roll]) + self.register_success_conditions( + [DetectedCondition(roll, success_sensor), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + self.boundary_roll.clear() + self.boundary_stand.clear() + self.boundary_roll.sample(self.roll_box) + self.boundary_stand.sample(self.stand_base) + return ['put toilet roll on stand', + 'place the toilet roll on the stand', + 'slide the roll onto its holder'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_tray_in_oven.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_tray_in_oven.py new file mode 100644 index 0000000000000000000000000000000000000000..6e61be95d528d059ece2337c09f2d7f8c4dff372 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_tray_in_oven.py @@ -0,0 +1,39 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class PutTrayInOven(Task): + + def init_task(self) -> None: + success_detector = ProximitySensor('success') + self.tray = Shape('tray') + self.register_graspable_objects([self.tray]) + self.register_success_conditions( + [DetectedCondition(self.tray, success_detector), + NothingGrasped(self.robot.gripper)]) + self.boundary = SpawnBoundary([Shape('oven_tray_boundary')]) + + def init_episode(self, index: int) -> List[str]: + self.boundary.clear() + self.boundary.sample( + self.tray, min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + return ['put tray in oven', + 'place the tray in the oven', + 'open the oven, then slide the tray in', + 'open the oven door, pick up the tray, and put it down on the ' + 'oven shelf'] + + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 4.], [0, 0, 3.14 / 4.] + + def boundary_root(self) -> Object: + return Shape('oven_boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_umbrella_in_umbrella_stand.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_umbrella_in_umbrella_stand.py new file mode 100644 index 0000000000000000000000000000000000000000..ad56d5021d8d4f088564208065a94a735e6e1e29 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/put_umbrella_in_umbrella_stand.py @@ -0,0 +1,25 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class PutUmbrellaInUmbrellaStand(Task): + + def init_task(self): + success_sensor = ProximitySensor('success') + umbrella = Shape('umbrella') + self.register_graspable_objects([umbrella]) + self.register_success_conditions( + [DetectedCondition(umbrella, success_sensor), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['put umbrella in umbrella stand' + 'pick up the umbrella and drop it in its stand', + 'use the handle to lift the umbrella and place it in its holder' + 'set the umbrella down inside the umbrella stand'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/reach_and_drag.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/reach_and_drag.py new file mode 100644 index 0000000000000000000000000000000000000000..4e7272364d20919a7a3e841b10b5b98da0b7bd07 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/reach_and_drag.py @@ -0,0 +1,45 @@ +from typing import List +from rlbench.backend.task import Task +from rlbench.const import colors +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor + + +class ReachAndDrag(Task): + + def init_task(self) -> None: + self.stick = Shape('stick') + self.register_graspable_objects([self.stick]) + self.cube = Shape('cube') + self.target = Shape('target0') + self.distractor1 = Shape('distractor1') + self.distractor2 = Shape('distractor2') + self.distractor3 = Shape('distractor3') + + def init_episode(self, index: int) -> List[str]: + self.register_success_conditions([ + DetectedCondition(self.cube, ProximitySensor('success0'))]) + color_name, color_rgb = colors[index] + self.target.set_color(color_rgb) + + _, distractor1_rgb = colors[(index + 5) % len(colors)] + self.distractor1.set_color(distractor1_rgb) + + _, distractor2_rgb = colors[(index + 6) % len(colors)] + self.distractor2.set_color(distractor2_rgb) + + _, distractor3_rgb = colors[(index + 7) % len(colors)] + self.distractor3.set_color(distractor3_rgb) + + return ['use the stick to drag the cube onto the %s target' + % color_name, + 'pick up the stick and use it to push or pull the cube ' + 'onto the %s target' % color_name, + 'drag the block towards the %s square on the table top' + % color_name, + 'grasping the stick by one end, pick it up and use the its ' + 'other end to move the block onto the %s target' % color_name] + + def variation_count(self) -> int: + return len(colors) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/reach_target.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/reach_target.py new file mode 100644 index 0000000000000000000000000000000000000000..8e1754a264cc4c48dc96daa7205b945d35abfb4a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/reach_target.py @@ -0,0 +1,51 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.const import colors +from rlbench.backend.task import Task +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.conditions import DetectedCondition + + +class ReachTarget(Task): + + def init_task(self) -> None: + self.target = Shape('target') + self.distractor0 = Shape('distractor0') + self.distractor1 = Shape('distractor1') + self.boundaries = Shape('boundary') + success_sensor = ProximitySensor('success') + self.register_success_conditions( + [DetectedCondition(self.robot.arm.get_tip(), success_sensor)]) + + def init_episode(self, index: int) -> List[str]: + color_name, color_rgb = colors[index] + self.target.set_color(color_rgb) + color_choices = np.random.choice( + list(range(index)) + list(range(index + 1, len(colors))), + size=2, replace=False) + for ob, i in zip([self.distractor0, self.distractor1], color_choices): + name, rgb = colors[i] + ob.set_color(rgb) + b = SpawnBoundary([self.boundaries]) + for ob in [self.target, self.distractor0, self.distractor1]: + b.sample(ob, min_distance=0.2, + min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + + return ['reach the %s target' % color_name, + 'touch the %s ball with the panda gripper' % color_name, + 'reach the %s sphere' %color_name] + + def variation_count(self) -> int: + return len(colors) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, 0.0] + + def get_low_dim_state(self) -> np.ndarray: + # One of the few tasks that have a custom low_dim_state function. + return np.array(self.target.get_position()) + + def is_static_workspace(self) -> bool: + return True diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/remove_cups.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/remove_cups.py new file mode 100644 index 0000000000000000000000000000000000000000..d810f341d35607da51902d312797bd7f32fd50b3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/remove_cups.py @@ -0,0 +1,109 @@ +from typing import List, Tuple +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + +MAX_CUPS_TO_REMOVE = 2 + + +class RemoveCups(Task): + + def init_task(self) -> None: + self.cups_removed = -1 + self.cups = [Shape('mug%d' % i) for i in range(3)] + self.spokes = [Shape('place_cups_holder_spoke%d' % i) for i in range(3)] + self.holder = Shape('place_cups_holder_base') + self.holder_boundary = Shape('tree_boundary') + self.register_graspable_objects(self.cups) + self.success_detectors = [ProximitySensor('success_detector%d' % i) + for i in range(3)] + self.w1 = Dummy('waypoint1') + self.w1_rel_pos = self.w1.get_position(relative_to=self.cups[0]) + self.w1_rel_ori = self.w1.get_orientation(relative_to=self.cups[0]) + self.w2 = Dummy('waypoint2') + self.w2_rel_pos = self.w2.get_position(relative_to=self.spokes[0]) + self.w2_rel_ori = self.w2.get_orientation(relative_to=self.spokes[0]) + self.w5 = Dummy('waypoint5') + self.w5_rel_pos = self.w5.get_orientation( + relative_to=self.success_detectors[0]) + self.w5_new_pos = self.w5.get_position() + self.w5_new_pos_saved = self.w5_new_pos + self.success_conditions = [NothingGrasped(self.robot.gripper)] + + def init_episode(self, index: int) -> List[str]: + self.cups_removed = -1 + self.cups_to_remove = 1 + index % MAX_CUPS_TO_REMOVE + self.w5_new_pos = self.w5_new_pos_saved + self.w1.set_position(self.w1_rel_pos, + relative_to=self.cups[0], + reset_dynamics=False) + for i in range(self.cups_to_remove): + self.success_conditions.append( + DetectedCondition(self.cups[i], self.success_detectors[i]) + ) + self.register_success_conditions(self.success_conditions) + self.register_waypoint_ability_start(0, self._move_above_next_target) + self.register_waypoints_should_repeat(self._repeat) + + if self.cups_to_remove == 1: + return ['remove 1 cup from the cup holder and place it on the ' + 'table', + 'remove one cup from the mug holder', + 'pick up 1 cup from the mug tree and place it on the table', + 'slide 1 mug off of its spoke on the cup holder and leave ' + 'it on the table top'] + else: + return ['remove %d cups from the cup holder and place it on the ' + 'table' % self.cups_to_remove, + 'remove %d cups from the cup holder' % self.cups_to_remove, + 'pick up %d cups from the mug tree and place them on the ' + 'table' % self.cups_to_remove, + 'slide %d mugs off of their spokes on the cup holder and ' + 'leave them on the table top' % self.cups_to_remove] + + def variation_count(self) -> int: + return MAX_CUPS_TO_REMOVE + + def _move_above_next_target(self, waypoint): + if self.cups_removed > self.cups_to_remove: + raise RuntimeError('Should not be here, all cups should have been ' + 'removed') + move_index = self.cups_removed if self.cups_removed > -1 else 0 + next_move_index = self.cups_removed + 1 if self.cups_removed > -1 else 0 + if self.cups_removed > -1: + self.w1.set_position(self.w1_rel_pos, + relative_to=self.cups[next_move_index], + reset_dynamics=False + ) + self.w1.set_orientation(self.w1_rel_ori, + relative_to=self.cups[next_move_index], + reset_dynamics=False + ) + self.w2.set_position(self.w2_rel_pos, + relative_to=self.spokes[next_move_index], + reset_dynamics=False) + self.w2.set_orientation(self.w2_rel_ori, + relative_to=self.spokes[next_move_index], + reset_dynamics=False + ) + new_x, new_y, _ = self.success_detectors[ + next_move_index].get_position() + self.w5_new_pos[0] = new_x + self.w5_new_pos[1] = new_y + self.w5.set_position(self.w5_new_pos, + reset_dynamics=False) + + ###### + self.cups_removed += 1 + ###### + + def cleanup(self) -> None: + self.cups_removed = -1 + + def _repeat(self): + return self.cups_removed < self.cups_to_remove - 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, 0.0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/scoop_with_spatula.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/scoop_with_spatula.py new file mode 100644 index 0000000000000000000000000000000000000000..8ddb7110d146e3e695439f73f1a70c5703b3137b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/scoop_with_spatula.py @@ -0,0 +1,27 @@ +from rlbench.backend.task import Task +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.conditions import GraspedCondition, DetectedCondition + + +class ScoopWithSpatula(Task): + + def init_task(self) -> None: + spatula = Shape('scoop_with_spatula_spatula') + self.register_graspable_objects([spatula]) + self.register_success_conditions([ + DetectedCondition(Shape('Cuboid'), ProximitySensor('success')), + GraspedCondition(self.robot.gripper, spatula) + ]) + + def init_episode(self, index: int) -> List[str]: + return ['scoop up the cube and lift it with the spatula', + 'scoop up the block and lift it with the spatula', + 'use the spatula to scoop the cube and lift it', + 'use the spatula to scoop the block and lift it', + 'pick up the cube using the spatula', + 'pick up the block using the spatula'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/screw_nail.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/screw_nail.py new file mode 100644 index 0000000000000000000000000000000000000000..b7dac8c76fa3fe6845fc2c3ba607ca62ff85144b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/screw_nail.py @@ -0,0 +1,36 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition, GraspedCondition, \ + ConditionSet + + +class ScrewNail(Task): + + def init_task(self) -> None: + screw_driver = Shape('screw_driver') + self.block = Shape('block') + self.register_graspable_objects([screw_driver]) + screw_joint = Joint('screw_joint') + + cond_set = ConditionSet([ + GraspedCondition(self.robot.gripper, screw_driver), + JointCondition(screw_joint, 1.4)], # about 90 degrees + order_matters=True) + self.register_success_conditions([cond_set]) + + def init_episode(self, index: int) -> List[str]: + return ['screw the nail in to the block', + 'pick up the screwdriver and screw in the protruding nail', + 'grasping the screw driver by its handle, slide its tip into ' + 'the groove on the end of the nail, and rotate the screw driver' + ' in an anti clockwise direction around the vertical axis', + 'lift the screwdriver up by its handle, lower its tip into the ' + 'protruding nail, and screw in the nail'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 2.], [0, 0, 3.14 / 2.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/set_clock_to_time.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/set_clock_to_time.py new file mode 100644 index 0000000000000000000000000000000000000000..6874a5582c3ba41b77364f92af067ded8d8f3005 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/set_clock_to_time.py @@ -0,0 +1,55 @@ +from typing import List, Tuple +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.task import Task +from pyrep.objects.dummy import Dummy + + +TIMES = ['15', '30', '45'] + +class SetClockToTime(Task): + + def init_task(self) -> None: + self._turn_points = [ + Dummy('point_15'), + Dummy('point_30'), + Dummy('point_45'), + ] + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + time = TIMES[index] + + self.register_fail_conditions([ + DetectedCondition(Shape('clock_needle_crank'), + ProximitySensor('Panda_gripper_attachProxSensor')) + ]) + + self.register_success_conditions([ + DetectedCondition(Shape('clock_minute_head'), + ProximitySensor('detector_%s' % time)), + NothingGrasped(self.robot.gripper) + ]) + + + turn_point = self._turn_points[self._variation_index] + waypoint3 = Dummy('waypoint2') + waypoint3.set_pose(turn_point.get_pose()) + + return [ + 'change the clock to show time 12.%s' % (time), + 'adjust the time to 12.%s' % (time), + 'change the clock to 12.%s' % (time), + 'set the clock to 12.%s' % (time), + 'turn the knob on the back of the clock until the time shows 12.%s' % (time), + 'rotate the wheel on the clock to make it show 12.%s' % (time), + 'make the clock say 12.%s' % (time), + 'turn the knob on the back of the clock %d degrees' % (90*(index+1)) + ] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -3.14/4 - 3.14/2], [0.0, 0.0, 3.14/4 - 3.14/2] \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/set_the_table.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/set_the_table.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb3503d342bf5e5559c3bcb2f3e1d12f93df025 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/set_the_table.py @@ -0,0 +1,42 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class SetTheTable(Task): + + def init_task(self) -> None: + plate = Shape('plate') + fork = Shape('fork') + knife = Shape('knife') + spoon = Shape('spoon') + glass = Shape('glass') + self.register_success_conditions([ + DetectedCondition(plate, ProximitySensor('plate_detector')), + DetectedCondition(fork, ProximitySensor('fork_detector')), + DetectedCondition(knife, ProximitySensor('knife_detector')), + DetectedCondition(spoon, ProximitySensor('spoon_detector')), + DetectedCondition(glass, ProximitySensor('glass_detector')), + NothingGrasped(self.robot.gripper)]) + self.register_graspable_objects([plate, fork, knife, spoon, glass]) + + def init_episode(self, index: int) -> List[str]: + return ['set the table' + 'place the dishes and cutlery on the table in preparation for ' + 'a meal', + 'pick up the plate and put it down on the table, then place ' + 'the fork to its left, the knife and then the spoon to its ' + 'right, and set the glass down just above them', + 'prepare the table for a meal', + 'arrange the plate, cutlery and glass neatly on the table ' + 'so that a person can eat', + 'get the table ready for lunch', + 'get the table ready for dinner'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, 0.0], [0.0, 0.0, 0.0] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/setup_checkers.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/setup_checkers.py new file mode 100644 index 0000000000000000000000000000000000000000..6d17e3ec7b50122785b57f8f5767937a1e0e3d84 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/setup_checkers.py @@ -0,0 +1,132 @@ +from typing import List +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + +MAX_CHECKERS_TO_SETUP = 3 + + +class SetupCheckers(Task): + + def init_task(self) -> None: + self.checkers_placed = -1 + self.checkers = [Shape('checker%d' % i) + for i in range(24)] + + self.chess_board = Shape('chess_board_base') + + self.checkers_starting_pos_list = [ + checker.get_position(self.chess_board) for checker in self.checkers] + self.checkers_starting_orientation = \ + self.checkers[0].get_orientation(self.chess_board) + + self.register_graspable_objects(self.checkers) + self.success_detectors = [ProximitySensor('detector%d' % i) + for i in range(24)] + self.success_conditions = [NothingGrasped(self.robot.gripper)] + for i in range(len(self.checkers)): + self.success_conditions.append( + DetectedCondition(self.checkers[i], self.success_detectors[i])) + + self.register_success_conditions(self.success_conditions) + self.cleanup() + + def init_episode(self, index: int) -> List[str]: + self.cleanup() + + self.checkers_to_setup = 1 + index % MAX_CHECKERS_TO_SETUP + + ## Use to showcase episode variations while rlbench backend gets fixed: + + self.register_waypoint_ability_start(0, self._move_above_next_target) + self.register_waypoints_should_repeat(self._repeat) + + target_checkers_unordered = np.random.choice(self.checkers, + self.checkers_to_setup, + replace=False) + self.target_indexes = [] + self.target_checkers = [] + for checker in self.checkers: + if checker in target_checkers_unordered: + self.target_checkers.append(checker) + + self.target_pos_list = [] + + pos_delta = 0.045 + start_pos_list = [+0.0070816, +0.31868, -0.094908] + + for i, checker in enumerate(self.checkers): + if checker in self.target_checkers: + self.target_indexes.append(i) + self.target_pos_list = checker.get_position() + checker.set_position(start_pos_list, + relative_to=self.chess_board, + reset_dynamics=False) + start_pos_list[2] += pos_delta + + if self.checkers_to_setup == 1: + rtn = ['place the remaining checker ' + 'in its initial position on the board'] + else: + rtn = ['place the %d remaining checkers in ' + 'their initial positions on the board' + % self.checkers_to_setup] + + rtn.extend(['prepare the checkers board', + 'get the chess board ready for a game of checkers', + 'setup the checkers board', 'setup checkers', + 'arrange the checkers ready for a game', + 'get checkers ready']) + + return rtn + + def variation_count(self) -> int: + return MAX_CHECKERS_TO_SETUP + + def cleanup(self) -> None: + self.checkers_placed = -1 + for i, checker in enumerate(self.checkers): + checker.set_position(self.checkers_starting_pos_list[i], + self.chess_board) + checker.set_orientation(self.checkers_starting_orientation, + self.chess_board) + + def _move_above_next_target(self, waypoint): + self.checkers_placed += 1 + self.target_index = self.target_indexes[self.checkers_placed] + + if self.checkers_placed > self.checkers_to_setup: + raise RuntimeError('Should not be here') + + w1 = Dummy('waypoint1') + # self.w2.set_parent(target_checkers[self.checkers_placed] + + unplaced_x, unplaced_y, unplaced_z = self.target_checkers[ + self.checkers_placed].get_position() + + z_offset_pickup = 0 + + w1.set_position([unplaced_x, unplaced_y, unplaced_z - z_offset_pickup], + reset_dynamics=False) + + w4 = Dummy('waypoint4') + + target_x, target_y, target_z = self.checkers_starting_pos_list[ + self.target_index] + z_offset_placement = 1.00 * 10 ** (-3) + + w4.set_position([target_x - z_offset_placement, target_y, target_z], + relative_to=self.chess_board, reset_dynamics=False) + + if self.checkers_to_setup > 1: + if self.target_index == self.target_indexes[0]: + self.target_index = self.target_indexes[1] + if self.target_index == self.target_indexes[1] \ + and self.checkers_to_setup == 3: + self.target_index = self.target_indexes[2] + + def _repeat(self): + return self.checkers_placed + 1 < self.checkers_to_setup diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/setup_chess.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/setup_chess.py new file mode 100644 index 0000000000000000000000000000000000000000..51a538a8f9a24c193da492512713eaca9404d1c3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/setup_chess.py @@ -0,0 +1,98 @@ +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy + +from rlbench.backend.conditions import DetectedCondition, NothingGrasped, ConditionSet +from rlbench.backend.task import Task + +from typing import List + +import numpy as np +import random + +COLORS = ['white', 'black'] +PIECES = ['king', 'kingside_bishop', 'kingside_rook', 'kingside_knight', 'pawn_a', + 'pawn_b', 'pawn_c', 'pawn_d', 'pawn_e', 'pawn_f', 'pawn_g', 'pawn_h', + 'queen', 'queenside_bishop', 'queenside_knight', 'queenside_rook'] + +class SetupChess(Task): + MAX_DISPLACEMENTS = 3 + + def init_task(self) -> None: + self.board = Shape('chess_board_base') + self.pieces = [Shape(f'{color}_{piece}_dynamic') for color in COLORS for piece in PIECES] + + self.positions = [piece.get_position(self.board) for piece in self.pieces] + self.rotations = [piece.get_orientation(self.board) for piece in self.pieces] + + self.success_detectors = [ProximitySensor(f'detector_{file}{rank}') for file in 'abcdefgh' for rank in [1, 2, 7, 8]] + self.success_conditions = [NothingGrasped(self.robot.gripper)] + + for piece, detector in zip(self.pieces, self.success_detectors): + x, y, z = piece.get_position(self.board) + z = detector.get_position(self.board)[2] + detector.set_position((x, y, z), self.board) + self.success_conditions.append(DetectedCondition(piece, detector)) + + self.register_success_conditions(self.success_conditions) + self.register_graspable_objects(self.pieces) + + def init_episode(self, index: int) -> List[str]: + for piece, position, rotation in zip(self.pieces, self.positions, self.rotations): + piece.set_position(position, self.board) + piece.set_orientation(rotation, self.board) + + self.nsetup = 1 + index % self.MAX_DISPLACEMENTS + self.placed = -1 + self.places = random.sample([(dx, dy) for dx in range(8) for dy in range(1, 3)], self.nsetup) + + self.register_waypoint_ability_start(0, self._move_above_next_target) + self.register_waypoints_should_repeat(self._repeat) + + self.indices = np.random.choice(np.arange(len(self.pieces)), self.nsetup, replace = False) + self.targets = [self.pieces[idx] for idx in self.indices] + + delta = 4.7888e-2 + + for piece, (x, y) in zip(self.targets, self.places): + x = -1.6759e-1 + x * delta + y = -7.1825e-2 + y * delta + z = piece.get_position(self.board)[2] + piece.set_position([x, y, z], self.board, reset_dynamics = False) + + if self.nsetup == 1: + cmds = ['place the remaining chess piece in its initial position on the board'] + else: + cmds = [f'place the {self.nsetup} remaining chess pieces in their initial positions on the board'] + + cmds.extend([ + 'prepare the chessboard', + 'get the chessboard ready for a game of chess', + 'setup the chessboard', + 'setup chess', + 'arrange the chess pieces ready for a game', + 'get chess pieces ready' + ]) + + return cmds + + def variation_count(self) -> int: + return self.MAX_DISPLACEMENTS + + def _move_above_next_target(self, waypoint): + self.placed += 1 + index = self.indices[self.placed] + piece = self.targets[self.placed] + final = self.positions[index] + + w1 = Dummy("waypoint1") + w4 = Dummy("waypoint4") + + xt, yt, zt = piece.get_position(self.board) # current position + xf, yf, zf = final # target position + + w1.set_position([xt, yt, zt], self.board, reset_dynamics = False) + w4.set_position([xf - 1e-3, yf, zf + 0.1], self.board, reset_dynamics = False) + + def _repeat(self): + return self.placed + 1 < self.nsetup diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_block_to_color_target.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_block_to_color_target.py new file mode 100644 index 0000000000000000000000000000000000000000..ca714183cfb32fb2e5d00c193b99d3c3f3aeb4c5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_block_to_color_target.py @@ -0,0 +1,61 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition +import numpy as np + + +class SlideBlockToColorTarget(Task): + + def init_task(self) -> None: + self.block = Shape('block') + self.target_colors = ['green', 'blue', 'pink', 'yellow'] + + self._waypoint_paths = { + 0: [Dummy('point1a'), + Dummy('point1b')], + + 1: [Dummy('point2a'), + Dummy('point2b'), + Dummy('point2c'), + Dummy('point2d'), + Dummy('point2e')], + + 2: [Dummy('point3a'), + Dummy('point3b')], + + 3: [Dummy('point4a'), + Dummy('point4b'), + Dummy('point4c'), + Dummy('point4d'), + Dummy('point4e')] + } + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + + self.register_success_conditions([ + DetectedCondition(Shape('block'), + ProximitySensor(f'success{self._variation_index+1}'))]) + + + target_color = self.target_colors[self._variation_index] + target_waypoints = self._waypoint_paths[self._variation_index] + + self._waypoints = [Dummy('waypoint%d'%(i)) + for i in range(5)] + + for i in range(len(target_waypoints)): + self._waypoints[i].set_pose(target_waypoints[i].get_pose()) + self.register_stop_at_waypoint(i+1) + + return ['slide the block to %s target' % (target_color), + 'slide the block onto the %s square' % (target_color), + 'push the block until it is sitting on top of the %s target' % (target_color), + 'slide the block towards the %s plane' % (target_color), + 'cover the %s target with the block by pushing the block in its direction' % (target_color)] + + def variation_count(self) -> int: + return 4 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_block_to_target.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_block_to_target.py new file mode 100644 index 0000000000000000000000000000000000000000..788dee80a56923f6fecb329d56235959421ced15 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_block_to_target.py @@ -0,0 +1,40 @@ +from typing import List + +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition + + +class SlideBlockToTarget(Task): + + def init_task(self) -> None: + self._block = Shape('block') + self._target = ProximitySensor('success') + self.register_success_conditions([ + DetectedCondition(self._block, self._target)]) + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + return ['slide the block to target', + 'slide the block onto the target', + 'push the block until it is sitting on top of the target', + 'slide the block towards the green target', + 'cover the target with the block by pushing the block in its' + ' direction'] + + def variation_count(self) -> int: + return 1 + + def get_low_dim_state(self) -> np.ndarray: + # One of the few tasks that have a custom low_dim_state function. + return np.concatenate([ + self._block.get_position(), self._target.get_position()]) + + def reward(self) -> float: + grip_to_block = -np.linalg.norm( + self._block.get_position() - self.robot.arm.get_tip().get_position()) + block_to_target = -np.linalg.norm( + self._block.get_position() - self._target.get_position()) + return grip_to_block + block_to_target diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_cabinet_open.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_cabinet_open.py new file mode 100644 index 0000000000000000000000000000000000000000..2922c6b03311ebf13e91a45ac8053398aec23f2f --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_cabinet_open.py @@ -0,0 +1,50 @@ +from typing import List, Tuple +from pyrep.objects.joint import Joint +from pyrep.objects.dummy import Dummy +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition, NothingGrasped + +OPTIONS = ['right', 'left'] + + +class SlideCabinetOpen(Task): + + def init_task(self): + self.left_joint = Joint('left_joint') + self.right_joint = Joint('right_joint') + self.waypoint0 = Dummy('waypoint0') + self.waypoint1 = Dummy('waypoint1') + self.waypoint2 = Dummy('waypoint2') + self.left_initial_waypoint = Dummy('waypoint4') + self.left_close_waypoint = Dummy('waypoint5') + self.left_far_waypoint = Dummy('waypoint6') + + def init_episode(self, index: int) -> List[str]: + option = OPTIONS[index] + + if option == 'left': + self.waypoint0.set_position( + self.left_initial_waypoint.get_position()) + self.waypoint1.set_position(self.left_close_waypoint.get_position()) + self.waypoint2.set_position(self.left_far_waypoint.get_position()) + self.register_success_conditions( + [JointCondition(self.left_joint, 0.06), + NothingGrasped(self.robot.gripper)]) + else: + self.register_success_conditions( + [JointCondition(self.right_joint, 0.06), + NothingGrasped(self.robot.gripper)]) + + return ['slide %s cabinet open' % option, + 'open the %s door' % option, + 'open the %s half of the cabinet' % option, + 'slide open the %s slide of the cabinet' % option, + 'grip the %s handle and slide the door open' % option, + 'grasp the %s door\'s handle and and drag it towards the middle' + ' of the cabinet in order to slide that door open' % option] + + def variation_count(self) -> int : + return 2 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -3.14 / 4.], [0.0, 0.0, 3.14 / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_cabinet_open_and_place_cups.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_cabinet_open_and_place_cups.py new file mode 100644 index 0000000000000000000000000000000000000000..a4991113982614bc2ab66dd333831102dc290836 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/slide_cabinet_open_and_place_cups.py @@ -0,0 +1,67 @@ +from typing import List, Tuple + +from pyrep.objects.joint import Joint +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, JointCondition, \ + NothingGrasped + +OPTIONS = ['left', 'right'] + + +class SlideCabinetOpenAndPlaceCups(Task): + + def init_task(self): + + self.cup = Shape('cup') + self.success_sensor = ProximitySensor('success') + self.register_graspable_objects([Shape('cup')]) + + self.left_joint = Joint('left_joint') + self.right_joint = Joint('right_joint') + self.waypoint0 = Dummy('waypoint0') + self.waypoint1 = Dummy('waypoint1') + self.waypoint2 = Dummy('waypoint2') + self.waypoint6 = Dummy('waypoint6') + self.left_initial_waypoint = Dummy('left_initial_waypoint') + self.left_close_waypoint = Dummy('left_close_waypoint') + self.left_far_waypoint = Dummy('left_far_waypoint') + self.place_cup_left_waypoint = Dummy('place_cup_left_waypoint') + + # self.register_success_conditions([ + # DetectedCondition(sel, ProximitySensor('success'))]) + + def init_episode(self, index: int) -> List[str]: + + option = OPTIONS[index] + + conditions = [DetectedCondition(self.cup, self.success_sensor), + NothingGrasped(self.robot.gripper)] + + if option == 'left': + self.waypoint0.set_position( + self.left_initial_waypoint.get_position()) + self.waypoint1.set_position(self.left_close_waypoint.get_position()) + self.waypoint2.set_position(self.left_far_waypoint.get_position()) + self.waypoint6.set_position(self.place_cup_left_waypoint.get_position()) + conditions.append(JointCondition(self.left_joint, 0.06)) + else: + conditions.append(JointCondition(self.right_joint, 0.06)) + self.register_success_conditions(conditions) + + return ['put cup in %s cabinet' % option, + 'put the mug away in the %s half of the cabinet' % option, + 'open the %s side of the cabinet and put the cup away in it' + % option, + 'grasping the %s handle, open the cabinet, then pick up the cup' + ' and set it down inside the cabinet' % option, + 'slide open the %s door on the cabinet and put away the mug' + % option] + + def variation_count(self) -> int: + return 2 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -3.14 / 4.], [0.0, 0.0, 3.14 / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/solve_puzzle.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/solve_puzzle.py new file mode 100644 index 0000000000000000000000000000000000000000..4c36e32319ae25ce8d832bfa0fe0a111828c01f2 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/solve_puzzle.py @@ -0,0 +1,28 @@ +from typing import List + +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape + +from rlbench.backend.conditions import NothingGrasped, DetectedCondition +from rlbench.backend.task import Task + + +class SolvePuzzle(Task): + + def init_task(self) -> None: + piece2 = Shape('solve_puzzle_piece2') + self.register_graspable_objects([piece2]) + self.register_success_conditions([ + NothingGrasped(self.robot.gripper), + DetectedCondition(piece2, ProximitySensor('success')) + ]) + + def init_episode(self, index: int) -> List[str]: + return [ + 'solve the puzzle', + 'put the puzzle piece into the puzzle', + 'pick up the puzzle piece and place it on the puzzle' + ] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_blocks.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..991a7e7c4932912172cee5ec1342a3fe40a13d7c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_blocks.py @@ -0,0 +1,108 @@ +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.dummy import Dummy +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedSeveralCondition +from rlbench.backend.conditions import NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.const import colors + +MAX_STACKED_BLOCKS = 3 +DISTRACTORS = 4 + + +class StackBlocks(Task): + + def init_task(self) -> None: + self.blocks_stacked = 0 + self.target_blocks = [Shape('stack_blocks_target%d' % i) + for i in range(4)] + self.distractors = [ + Shape('stack_blocks_distractor%d' % i) + for i in range(DISTRACTORS)] + + self.boundaries = [Shape('stack_blocks_boundary%d' % i) + for i in range(4)] + + self.register_graspable_objects(self.target_blocks + self.distractors) + + self.register_waypoint_ability_start(0, self._move_above_next_target) + self.register_waypoint_ability_start(3, self._move_above_drop_zone) + self.register_waypoint_ability_start(5, self._is_last) + self.register_waypoints_should_repeat(self._repeat) + + def init_episode(self, index: int) -> List[str]: + # For each color, we want to have 2, 3 or 4 blocks stacked + color_index = int(index / MAX_STACKED_BLOCKS) + self.blocks_to_stack = 2 + index % MAX_STACKED_BLOCKS + color_name, color_rgb = colors[color_index] + for b in self.target_blocks: + b.set_color(color_rgb) + + success_detector = ProximitySensor( + 'stack_blocks_success') + self.register_success_conditions([DetectedSeveralCondition( + self.target_blocks, success_detector, self.blocks_to_stack), + NothingGrasped(self.robot.gripper) + ]) + + self.blocks_stacked = 0 + color_choices = np.random.choice( + list(range(color_index)) + list( + range(color_index + 1, len(colors))), + size=2, replace=False) + for i, ob in enumerate(self.distractors): + name, rgb = colors[color_choices[int(i / 4)]] + ob.set_color(rgb) + b = SpawnBoundary(self.boundaries) + for block in self.target_blocks + self.distractors: + b.sample(block, min_distance=0.1) + + return ['stack %d %s blocks' % (self.blocks_to_stack, color_name), + 'place %d of the %s cubes on top of each other' + % (self.blocks_to_stack, color_name), + 'pick up and set down %d %s blocks on top of each other' + % (self.blocks_to_stack, color_name), + 'build a tall tower out of %d %s cubes' + % (self.blocks_to_stack, color_name), + 'arrange %d %s blocks in a vertical stack on the table top' + % (self.blocks_to_stack, color_name), + 'set %d %s cubes on top of each other' + % (self.blocks_to_stack, color_name)] + + def variation_count(self) -> int: + return len(colors) * MAX_STACKED_BLOCKS + + def _move_above_next_target(self, _): + if self.blocks_stacked >= self.blocks_to_stack: + raise RuntimeError('Should not be here.') + w2 = Dummy('waypoint1') + x, y, z = self.target_blocks[self.blocks_stacked].get_position() + _, _, oz = self.target_blocks[self.blocks_stacked].get_orientation() + ox, oy, _ = w2.get_orientation() + w2.set_position([x, y, z]) + w2.set_orientation([ox, oy, -oz]) + + def _move_above_drop_zone(self, waypoint): + target = Shape('stack_blocks_target_plane') + x, y, z = target.get_position() + waypoint.get_waypoint_object().set_position( + [x, y, z + 0.08 + 0.06 * self.blocks_stacked + 0.05]) + + def _is_last(self, waypoint): + last = self.blocks_stacked == self.blocks_to_stack - 1 + waypoint.skip = last + + def _is_last(self, waypoint): + last = self.blocks_stacked == self.blocks_to_stack - 1 + waypoint.skip = last + + def _is_last(self, waypoint): + last = self.blocks_stacked == self.blocks_to_stack - 1 + waypoint.skip = last + + def _repeat(self): + self.blocks_stacked += 1 + return self.blocks_stacked < self.blocks_to_stack \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_chairs.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_chairs.py new file mode 100644 index 0000000000000000000000000000000000000000..0a52da7c8b495da599a39616efc5d2b39339bb07 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_chairs.py @@ -0,0 +1,74 @@ + +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape + +from rlbench.backend.conditions import DetectedCondition, NothingGrasped, Condition +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task +from rlbench.const import colors + +from typing import List + +import numpy as np + +class ChairsOrientedCondition(Condition): + def __init__(self, objs: List[Object], error: float): + self.objs = objs + self.error = error + + def condition_met(self): + for obj in self.objs[:-1]: + x, y, z = obj.get_orientation(self.objs[-1]) + if abs(x) > self.error or abs(y) > self.error or abs(z) > self.error: + return False, False + return True, False + +class StackChairs(Task): + def init_task(self) -> None: + self.chair1 = Shape('chair1') + self.chair2 = Shape('chair2') + self.chair3 = Shape('chair3') + + self.cushions1 = Shape('cushions1') + self.cushions2 = Shape('cushions2') + self.cushions3 = Shape('cushions3') + + self.boundary = SpawnBoundary([Shape('boundary')]) + self.detector = ProximitySensor('success') + + self.register_graspable_objects([self.chair1, self.chair2, self.chair3]) + self.register_success_conditions([ + DetectedCondition(self.chair1, self.detector), + DetectedCondition(self.chair2, self.detector), + NothingGrasped(self.robot.gripper), + ChairsOrientedCondition([self.chair1, self.chair2, self.chair3], error = 0.25) + ]) + + def init_episode(self, index: int) -> List[str]: + indices = np.random.choice(np.arange(len(colors)), size = 2, replace = False) + + color1, rgb1 = colors[index] + color2, rgb2 = colors[indices[0]] + color3, rgb3 = colors[indices[1]] # target chair color + + self.cushions1.set_color(rgb1) + self.cushions2.set_color(rgb2) + self.cushions3.set_color(rgb3) + + self.boundary.clear() + self.boundary.sample(self.chair1, min_distance=0.1, + min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + self.boundary.sample(self.chair2, min_distance=0.1, + min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + self.boundary.sample(self.chair3, min_distance=0.1, + min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + + return [f'stack the other chairs on top of the {color1} chair', + f'put the remaining two chairs on top of the {color1} chair', + f'pick up and set the chairs down onto the {color1} chair', + f'create a stack of chairs with the {color1} chair at its base', + f'keeping the {color1} chair on the table, stack the other two onto it'] + + def variation_count(self) -> int: + return len(colors) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_cups.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_cups.py new file mode 100644 index 0000000000000000000000000000000000000000..8cb4f852afe74361c5b00a23ed4907cae01baf61 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_cups.py @@ -0,0 +1,69 @@ +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.const import colors +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + + +class StackCups(Task): + + def init_task(self) -> None: + success_sensor = ProximitySensor('success') + self.cup1 = Shape('cup1') + self.cup2 = Shape('cup2') + self.cup3 = Shape('cup3') + self.cup1_visual = Shape('cup1_visual') + self.cup2_visual = Shape('cup2_visual') + self.cup3_visaul = Shape('cup3_visual') + + self.boundary = SpawnBoundary([Shape('boundary')]) + + self.register_graspable_objects([self.cup1, self.cup2, self.cup3]) + self.register_success_conditions([ + DetectedCondition(self.cup1, success_sensor), + DetectedCondition(self.cup3, success_sensor), + NothingGrasped(self.robot.gripper) + ]) + + def init_episode(self, index: int) -> List[str]: + self.variation_index = index + target_color_name, target_rgb = colors[index] + + random_idx = np.random.choice(len(colors)) + while random_idx == index: + random_idx = np.random.choice(len(colors)) + _, other1_rgb = colors[random_idx] + + random_idx = np.random.choice(len(colors)) + while random_idx == index: + random_idx = np.random.choice(len(colors)) + _, other2_rgb = colors[random_idx] + + self.cup2_visual.set_color(target_rgb) + self.cup1_visual.set_color(other1_rgb) + self.cup3_visaul.set_color(other2_rgb) + + self.boundary.clear() + self.boundary.sample(self.cup2, min_distance=0.05, + min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + self.boundary.sample(self.cup1, min_distance=0.05, + min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + self.boundary.sample(self.cup3, min_distance=0.05, + min_rotation=(0, 0, 0), max_rotation=(0, 0, 0)) + + return ['stack the other cups on top of the %s cup' % target_color_name, + 'place two of the cups onto the odd cup out', + 'put the remaining two cups on top of the %s cup' + % target_color_name, + 'pick up and set the cups down into the %s cup' + % target_color_name, + 'create a stack of cups with the %s cup as its base' + % target_color_name, + 'keeping the %s cup on the table, stack the other two onto it' + % target_color_name] + + def variation_count(self) -> int: + return len(colors) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_wine.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_wine.py new file mode 100644 index 0000000000000000000000000000000000000000..28ce8c5e129463cc7abff230b520f4ccce914d23 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/stack_wine.py @@ -0,0 +1,30 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class StackWine(Task): + + def init_task(self): + wine_bottle = Shape('wine_bottle') + self.register_graspable_objects([wine_bottle]) + self.register_success_conditions( + [DetectedCondition(wine_bottle, ProximitySensor('success'))]) + + def init_episode(self, index: int) -> List[str]: + return ['stack wine bottle', + 'slide the bottle onto the wine rack', + 'put the wine away', + 'leave the wine on the shelf', + 'grasp the bottle and put it away', + 'place the wine bottle on the wine rack'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 4.], [0, 0, np.pi / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/straighten_rope.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/straighten_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..1a646cc082bde5630c7be52d99cf1d3bffac947b --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/straighten_rope.py @@ -0,0 +1,25 @@ +from typing import List +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task + + +class StraightenRope(Task): + + def init_task(self) -> None: + self.register_success_conditions( + [DetectedCondition(Shape('head'), ProximitySensor('success_head')), + DetectedCondition(Shape('tail'), ProximitySensor('success_tail'))]) + + def init_episode(self, index: int) -> List[str]: + return ['straighten rope', + 'pull the rope straight', + 'grasping each end of the rope in turn, leave the rope straight' + ' on the table', + 'pull each end of the rope until is is straight', + 'tighten the rope', + 'pull the rope tight'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/sweep_to_dustpan.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/sweep_to_dustpan.py new file mode 100644 index 0000000000000000000000000000000000000000..677e6eaf32f39870d71a6746f1cf4567e625f2b3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/sweep_to_dustpan.py @@ -0,0 +1,31 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition + +DIRT_NUM = 5 + + +class SweepToDustpan(Task): + + def init_task(self) -> None: + broom = Shape('broom') + success_sensor = ProximitySensor('success') + dirts = [Shape('dirt' + str(i)) for i in range(DIRT_NUM)] + conditions = [DetectedCondition(dirt, success_sensor) for dirt in dirts] + self.register_graspable_objects([broom]) + self.register_success_conditions(conditions) + + def init_episode(self, index: int) -> List[str]: + return ['sweep dirt to dustpan', + 'sweep the dirt up', + 'use the broom to brush the dirt into the dustpan', + 'clean up the dirt', + 'pick up the brush and clean up the table', + 'grasping the broom by its handle, clear way the dirt from the ' + 'table', + 'leave the table clean'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/sweep_to_dustpan_of_size.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/sweep_to_dustpan_of_size.py new file mode 100644 index 0000000000000000000000000000000000000000..617995d81538a4821e3fc5f17e7b6fff5d7b6aff --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/sweep_to_dustpan_of_size.py @@ -0,0 +1,56 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from pyrep.objects.object import Object +from pyrep.objects.dummy import Dummy +from rlbench.backend.conditions import DetectedCondition + +DIRT_NUM = 5 + + +class SweepToDustpanOfSize(Task): + + def init_task(self) -> None: + self._dustpan_sizes = ['tall', 'short'] + + broom = Shape('broom') + self.register_graspable_objects([broom]) + + self._waypoint_paths = { + 0: [Dummy('point1a'), + Dummy('point1b'), + Dummy('point1c')], + + 1: [Dummy('point2a'), + Dummy('point2b'), + Dummy('point2c')] + } + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + dustpan_size = self._dustpan_sizes[self._variation_index] + + success_sensor = ProximitySensor(f'success_{dustpan_size}') + dirts = [Shape('dirt' + str(i)) for i in range(DIRT_NUM)] + conditions = [DetectedCondition(dirt, success_sensor) for dirt in dirts] + self.register_success_conditions(conditions) + + target_waypoints = self._waypoint_paths[self._variation_index] + self._waypoints = [Dummy('waypoint%d'%(i)) + for i in range(2, 5)] + + for i in range(len(target_waypoints)): + self._waypoints[i].set_pose(target_waypoints[i].get_pose()) + self.register_stop_at_waypoint(2+i+1) + + return ['sweep dirt to the %s dustpan' % (dustpan_size), + 'sweep the dirt up into the %s dustpan' % (dustpan_size), + 'use the broom to brush the dirt into the %s dustpan' % (dustpan_size), + 'clean up the dirt with the %s pan' % (dustpan_size)] + + def variation_count(self) -> int: + return 2 + + # def boundary_root(self) -> Object: + # return Shape('boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_cup_out_from_cabinet.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_cup_out_from_cabinet.py new file mode 100644 index 0000000000000000000000000000000000000000..c38ec0b8e644b80b77ee13a7e2f6e1ba2698360e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_cup_out_from_cabinet.py @@ -0,0 +1,49 @@ +from typing import List, Tuple +from pyrep.objects.dummy import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + +OPTIONS = ['left', 'right'] + + +class TakeCupOutFromCabinet(Task): + + def init_task(self) -> None: + self.cup = Shape('cup') + self.left_placeholder = Dummy('left_cup_placeholder') + self.waypoint1 = Dummy('waypoint1') + self.waypoint2 = Dummy('waypoint2') + self.left_way_placeholder1 = Dummy('left_way_placeholder1') + self.left_way_placeholder2 = Dummy('left_way_placeholder2') + + self.register_success_conditions( + [DetectedCondition( + self.cup, ProximitySensor('success'), negated=True), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + option = OPTIONS[index] + + if option == 'left': + self.waypoint1.set_position( + self.left_way_placeholder1.get_position()) + self.waypoint2.set_position( + self.left_way_placeholder2.get_position()) + self.cup.set_position(self.left_placeholder.get_position()) + + return ['take out a cup from the %s half of the cabinet' % option, + 'open the %s side of the cabinet and get the cup' + % option, + 'grasping the %s handle, open the cabinet, then retrieve the ' + 'cup' % option, + 'slide open the %s door on the cabinet and put take the cup out' + % option, + 'remove the cup from the %s part of the cabinet' % option] + + def variation_count(self) -> int: + return 2 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -3.14/2], [0.0, 0.0, 3.14/2] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_frame_off_hanger.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_frame_off_hanger.py new file mode 100644 index 0000000000000000000000000000000000000000..4dad06b2d5faa2938c76b3a899db949e9a0952e7 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_frame_off_hanger.py @@ -0,0 +1,31 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class TakeFrameOffHanger(Task): + + def init_task(self) -> None: + frame = Shape('frame') + self.register_graspable_objects([frame]) + self.register_success_conditions( + [DetectedCondition(frame, ProximitySensor('hanger_detector'), + negated=True), + DetectedCondition(frame, ProximitySensor('success')), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['take frame off hanger', + 'slide the photo off of the hanger and set it down on the ' + 'table', + 'grab a hold of the frame, remove it from the hanger and put it' + ' down', + 'grasping the picture frame, take it off the wall and place it' + 'on the table top', + 'take the picture down', + 'remove the photo frame from the wall'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_item_out_of_drawer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_item_out_of_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..e69801f5a64ac400afd912aae753b6a6c2807a70 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_item_out_of_drawer.py @@ -0,0 +1,47 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task + + +class TakeItemOutOfDrawer(Task): + + def init_task(self) -> None: + self._options = ['bottom', 'middle', 'top'] + self._anchors = [Dummy('waypoint_anchor_%s' % opt) + for opt in self._options] + self._joints = [Joint('drawer_joint_%s' % opt) + for opt in self._options] + self._waypoint1 = Dummy('waypoint1') + self._item = Shape('item') + self.register_graspable_objects([self._item]) + self.register_success_conditions( + [DetectedCondition(self._item, ProximitySensor('success'))]) + + def init_episode(self, index: int) -> List[str]: + option = self._options[index] + anchor = self._anchors[index] + self._waypoint1.set_position(anchor.get_position()) + _, _, z_target = anchor.get_position() + x, y, z = self._item.get_position() + self._item.set_position([x, y, z_target]) + return ['take item out of the %s drawer' % option, + 'open the %s drawer and take the cube out' % option, + 'grasp the %s handle on the drawers, pull the drawer open, and' + ' take out what you find inside' % option, + 'clear out the %s drawer' % option, + 'make sure the %s drawer is empty' % option, + 'lift the block out of the %s drawer' % option, + 'using the handle, open the %s drawer, and remove any items #' + 'inside' % option] + + def variation_count(self) -> int: + return 3 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, - np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_lid_off_saucepan.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_lid_off_saucepan.py new file mode 100644 index 0000000000000000000000000000000000000000..22978b6283f3be0e64427dfec6c58d65c3767729 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_lid_off_saucepan.py @@ -0,0 +1,39 @@ +from typing import List + +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import DetectedCondition, ConditionSet, \ + GraspedCondition +from rlbench.backend.task import Task + + +class TakeLidOffSaucepan(Task): + + def init_task(self) -> None: + self.lid = Shape('saucepan_lid_grasp_point') + self.success_detector = ProximitySensor('success') + self.register_graspable_objects([self.lid]) + cond_set = ConditionSet([ + GraspedCondition(self.robot.gripper, self.lid), + DetectedCondition(self.lid, self.success_detector) + ]) + self.register_success_conditions([cond_set]) + + def init_episode(self, index: int) -> List[str]: + return ['take lid off the saucepan', + 'using the handle, lift the lid off of the pan', + 'remove the lid from the pan', + 'grip the saucepan\'s lid and remove it from the pan', + 'leave the pan open', + 'uncover the saucepan'] + + def variation_count(self) -> int: + return 1 + + def reward(self) -> float: + grasp_lid_reward = -np.linalg.norm( + self.lid.get_position() - self.robot.arm.get_tip().get_position()) + lift_lid_reward = -np.linalg.norm( + self.lid.get_position() - self.success_detector.get_position()) + return grasp_lid_reward + lift_lid_reward diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_money_out_safe.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_money_out_safe.py new file mode 100644 index 0000000000000000000000000000000000000000..7845cecb2397c2609ef0f182ca61e3bd4d5f1666 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_money_out_safe.py @@ -0,0 +1,114 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + +NUM_SHELVES_IN_SAFE = 3 + + +class TakeMoneyOutSafe(Task): + + def init_task(self) -> None: + self.shelf = {0: 'bottom', 1: 'middle', 2: 'top'} + self.shelf_alt = {0: 'first', 1: 'second', 2: 'third'} + self.money_list = [Shape('dollar_stack%d' % i) for i in range(3)] + self.register_graspable_objects(self.money_list) + self.success_conditions = [NothingGrasped(self.robot.gripper)] + + self.w1 = Dummy('waypoint1') + self.w1_rel_pos = [-2.7287 * 10 ** (-4), -2.3246 * 10 ** (-6), + +4.5627 * 10 ** (-2)] + self.w1_rel_ori = [-3.1416, 7.2824 * 10 ** (-1), -2.1265 * 10 ** (-2)] + self.w3 = Dummy('waypoint3') + self.place_boundary = Shape('placement_boundary') + self.pick_boundary = Shape('money_boundary') + self.safe = Shape('safe_body') + self.money_ori = self.money_list[0].get_orientation() + + def init_episode(self, index: int) -> List[str]: + self.target_shelf = index + target_dummy_name = 'dummy_shelf' + str(self.target_shelf) + target_pos_dummy = Dummy(target_dummy_name) + target_pos = target_pos_dummy.get_position() + target_ori = target_pos_dummy.get_orientation() + self.w1.set_position(target_pos, reset_dynamics=False) + self.w1.set_orientation(target_ori, reset_dynamics=False) + b_place = SpawnBoundary([self.place_boundary]) + b_place.sample(self.w3, min_rotation=(0.0, 0.0, -np.pi / 12), + max_rotation=(0.0, 0.0, +np.pi / 12), min_distance=0) + self.success_detector = ProximitySensor('success_detector') + while len(self.success_conditions) > 1: + self.success_conditions.pop() + self.success_conditions.append( + DetectedCondition(self.money_list[self.target_shelf], + self.success_detector) + ) + self.register_success_conditions(self.success_conditions) + # self.w1.set_parent(target_pos_dummy, keep_in_place=True) + rel_target_pos = self.w1.get_position( + relative_to=self.money_list[index]) + rel_target_ori = self.w1.get_orientation( + relative_to=self.money_list[index] + ) + b_pick = SpawnBoundary([self.pick_boundary]) + b_pick.sample(self.money_list[index], + min_rotation=(0.0, 0.0, -np.pi / 10), + max_rotation=(0.0, 0.0, 0.0), min_distance=0) + + for m in self.money_list: + b_pick.sample(m, + min_rotation=(0.0, 0.0, -np.pi/10), + max_rotation=(0.0, 0.0, 0.0), min_distance=0) + + self.w1.set_position(rel_target_pos, relative_to=self.money_list[index], + reset_dynamics=False) + self.w1.set_orientation(rel_target_ori, + relative_to=self.money_list[index], + reset_dynamics=False) + + """ + if self.target_shelf == 0: + return ['take the money out of the bottom shelf and place it on ' + 'the table'] + elif self.target_shelf == 1: + return ['take the money out of the middle shelf and place it on ' + 'the table'] + elif self.target_shelf == 2: + return ['take the money out of the top shelf and place it on ' + 'the table'] + else: + raise ValueError('Invalid target_shelf index, should not be here') + return -1 + """ + + return ['take the money out of the %s shelf and place it on the ' + 'table' % self.shelf[index], + 'take the stack of money out of the %s shelf and place it on ' + 'the table' % self.shelf_alt[index], + 'take one of the stacks of money out of the %s bit of the safe' + % self.shelf[index], + 'take the money off of the %s shelf of the safe' + % self.shelf_alt[index], + 'grasp the bank notes in the %s and remove it from the safe' + % self.shelf[index], + 'get the money from the %s shelf' % self.shelf_alt[index], + 'retrieve the stack of bank notes from the %s of the safe' + % self.shelf[index], + 'locate the money on the %s shelf, grasp it, remove if from the' + ' safe, and set it on the table' % self.shelf[index], + 'put the money on the %s shelf down on the table' + % self.shelf_alt[index]] + + def variation_count(self) -> int: + return NUM_SHELVES_IN_SAFE + + def cleanup(self) -> None: + for m in self.money_list: + m.set_orientation(self.money_ori, reset_dynamics=False) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return (0.0, 0.0, -0.5 * np.pi), (0.0, 0.0, +0.5 * np.pi) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_off_weighing_scales.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_off_weighing_scales.py new file mode 100644 index 0000000000000000000000000000000000000000..c471bcce72c471b3a8ce9e36f4673239ba309903 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_off_weighing_scales.py @@ -0,0 +1,90 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + +UNIQUE_PEPPERS_TO_TAKE_OFF = 3 + + +class TakeOffWeighingScales(Task): + + def init_task(self) -> None: + self.needle = Shape('scales_meter_needle') + self.needle_pivot = Shape('scales_meter_pivot') + self.top_plate = Shape('scales_tray_visual') + self.joint = Joint('scales_joint') + _, _, starting_z = self.top_plate.get_position() + self.top_plate_starting_z = starting_z + self.needle_starting_ori = self.needle.get_orientation( + relative_to=self.needle_pivot) + self.peppers = [Shape('pepper%d' % i) for i in range(3)] + self.register_graspable_objects(self.peppers) + self.boundary = Shape('peppers_boundary') + self.success_detector = ProximitySensor('success_detector') + self.success_conditions = [NothingGrasped(self.robot.gripper)] + self.w0 = Dummy('waypoint0') + self.w0_rel_pos = self.w0.get_position(relative_to=self.peppers[0]) + self.w0_rel_ori = self.w0.get_orientation(relative_to=self.peppers[0]) + + def init_episode(self, index: int) -> List[str]: + self._variation_index = index + self.target_pepper_index = index + while len(self.success_conditions) > 1: + self.success_conditions.pop() + self.success_conditions.append(DetectedCondition( + self.peppers[self.target_pepper_index], self.success_detector)) + self.register_success_conditions(self.success_conditions) + b = SpawnBoundary([self.boundary]) + for p in self.peppers: + b.sample(p, ignore_collisions=False, min_distance=0.09, + min_rotation=(0.00, 0.00, -3.14), + max_rotation=(0.00, 0.00, +3.14) + ) + self.w0.set_position(self.w0_rel_pos, + relative_to=self.peppers[ + self.target_pepper_index], + reset_dynamics=False) + self.w0.set_orientation(self.w0_rel_ori, + relative_to=self.peppers[ + self.target_pepper_index], + reset_dynamics=False) + index_dic = {0: 'green', 1: 'red', 2: 'yellow'} + if self.target_pepper_index in range(3): + return [ + 'remove the %s pepper from the weighing scales and place it ' + 'on the table' % index_dic[self.target_pepper_index], + 'take the %s pepper off of the scales' + % index_dic[self.target_pepper_index], + 'lift the %s pepper off of the tray and set it down on the ' + 'table' % index_dic[self.target_pepper_index], + 'grasp the %s pepper and move it to the table top' + % index_dic[self.target_pepper_index], + 'take the %s object off of the scales tray' + % index_dic[self.target_pepper_index], + 'put the %s item on the item' + % index_dic[self.target_pepper_index]] + else: + raise ValueError('Invalid pepper index, should not be here') + + def variation_count(self) -> int: + return UNIQUE_PEPPERS_TO_TAKE_OFF + + def step(self) -> None: + _, _, pos_z = self.top_plate.get_position() + dz = self.top_plate_starting_z - pos_z + d_alpha = -130 * dz + new_needle_ori = [self.needle_starting_ori[0] + d_alpha, + self.needle_starting_ori[1], + self.needle_starting_ori[2]] + + self.needle.set_orientation(new_needle_ori, + relative_to=self.needle_pivot, + reset_dynamics=False) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -0.25 * np.pi], [0.0, 0.0, +0.25 * np.pi] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_plate_off_colored_dish_rack.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_plate_off_colored_dish_rack.py new file mode 100644 index 0000000000000000000000000000000000000000..72d9dde88d0a6e9c5d0f3a44f35aec5e86b9b720 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_plate_off_colored_dish_rack.py @@ -0,0 +1,43 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + +OPTIONS = ['black', 'red', 'purple'] + + +class TakePlateOffColoredDishRack(Task): + + def init_task(self) -> None: + self.plate = Shape('plate') + self.success_poses = [Dummy('success_pos%d' % i) + for i in range(3)] + self.register_graspable_objects([self.plate]) + self.register_success_conditions([ + DetectedCondition( + self.plate, ProximitySensor('success_source'), negated=True), + DetectedCondition(self.plate, ProximitySensor('success_target')), + NothingGrasped(self.robot.gripper) + ]) + + def init_episode(self, index: int) -> List[str]: + option = OPTIONS[index] + target_pos = self.success_poses[index] + x, y, _ = target_pos.get_position() + _, _, z = self.plate.get_position() + self.plate.set_position([x, y, z]) + return ['take plate off the %s colored rack' % option, + 'remove the dish from the %s rack' % option, + 'find the dish placed between the %s spokes of the dish rack ' + 'and drop it on the table' % option, + 'grasp the plate from the %s part of the dish rack, lift it up' + ' off of the rack, and leave it on the table top' % option, + 'move the plate from the %s rack to the table\'s surface' + % option, + 'lift the plate up off the %s colored rack and drop it onto the' + ' table from a considerable height' % option] + + def variation_count(self) -> int: + return 3 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_shoes_out_of_box.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_shoes_out_of_box.py new file mode 100644 index 0000000000000000000000000000000000000000..8147ba04560b86438b511e60fc7484b2bdf6e5ed --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_shoes_out_of_box.py @@ -0,0 +1,34 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class TakeShoesOutOfBox(Task): + + def init_task(self) -> None: + shoe1, shoe2 = Shape('shoe1'), Shape('shoe2') + self.register_graspable_objects([shoe1, shoe2]) + success_sensor = ProximitySensor('success_out_box') + self.register_success_conditions([ + DetectedCondition(shoe1, success_sensor), + DetectedCondition(shoe2, success_sensor), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['take shoes out of box', + 'open the shoe box and take the shoes out', + 'put the shoes found inside the box on the table', + 'set the shoes down on the table', + 'pick up the shoes from the box and put them down', + 'grasp the edge of the box lid to open it, then grasp each shoe' + ', lifting up out of the shoe box and leaving them down on the ' + 'table'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -np.pi / 8], [0, 0, np.pi / 8] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_toilet_roll_off_stand.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_toilet_roll_off_stand.py new file mode 100644 index 0000000000000000000000000000000000000000..ae130fd8750c328d6ee57dbdeafadde7649c30a5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_toilet_roll_off_stand.py @@ -0,0 +1,35 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape + +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.task import Task + + +class TakeToiletRollOffStand(Task): + + def init_task(self) -> None: + roll = Shape('toilet_roll') + success_sensor = ProximitySensor('success') + self.register_graspable_objects([roll]) + self.register_success_conditions( + [DetectedCondition(roll, success_sensor, negated=True)]) + + def init_episode(self, index: int) -> List[str]: + return ['take toilet roll off stand', + 'slide the toilet paper of its stand and set it down', + 'place the roll onto the table', + 'get the toilet paper roll and then leave it on the table', + 'remove the toilet roll from the holder', + 'get the toilet paper', + 'grasping the end of the roll, pull if off of the stand, and ' + 'set it on the table'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi), (0.0, 0.0, np.pi * 0.25) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_tray_out_of_oven.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_tray_out_of_oven.py new file mode 100644 index 0000000000000000000000000000000000000000..8581534ff1399a4a67cb5c33a76967fdc1b65cfc --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_tray_out_of_oven.py @@ -0,0 +1,36 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class TakeTrayOutOfOven(Task): + + def init_task(self) -> None: + success_detector = ProximitySensor('success') + tray = Shape('tray') + self.register_graspable_objects([tray]) + self.register_success_conditions( + [DetectedCondition(tray, success_detector, negated=True), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['take tray out of oven', + 'open the oven and take the baking tray out', + 'grasp the handle on the over door, open it, and remove the ' + 'tray from the oven', + 'get the baking tray from the oven', + 'get the tray', + 'take out the tray', + 'retrieve the oven tray'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 4.], [0, 0, 3.14 / 4.] + + def boundary_root(self) -> Object: + return Shape('oven_boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_umbrella_out_of_umbrella_stand.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_umbrella_out_of_umbrella_stand.py new file mode 100644 index 0000000000000000000000000000000000000000..4ff9d164acd9c3f27f83849b89342ac827a1fec2 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_umbrella_out_of_umbrella_stand.py @@ -0,0 +1,26 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition + + +class TakeUmbrellaOutOfUmbrellaStand(Task): + def init_task(self): + success_sensor = ProximitySensor('success') + umbrella = Shape('umbrella') + self.register_graspable_objects([umbrella]) + self.register_success_conditions( + [DetectedCondition(umbrella, success_sensor, negated=True)]) + + def init_episode(self, index: int) -> List[str]: + return ['take umbrella out of umbrella stand', + 'grasping the umbrella by its handle, lift it up and out of the' + ' stand', + 'remove the umbrella from the stand', + 'retrieve the umbrella from the stand', + 'get the umbrella', + 'lift the umbrella out of the stand'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_usb_out_of_computer.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_usb_out_of_computer.py new file mode 100644 index 0000000000000000000000000000000000000000..d8cce324b5f226e9641b695f01500df320d41069 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/take_usb_out_of_computer.py @@ -0,0 +1,26 @@ +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class TakeUsbOutOfComputer(Task): + + def init_task(self) -> None: + usb = Shape('usb') + self.register_graspable_objects([usb]) + self.register_success_conditions( + [DetectedCondition(usb, ProximitySensor('success'), negated=True), + NothingGrasped(self.robot.gripper)]) + + def init_episode(self, index: int) -> List[str]: + return ['take usb out of computer', + 'remove the usb stick from its port', + 'retrieve the usb stick', + 'grasp the usb stick and slide it out of the pc', + 'get a hold of the usb stick and pull it out of the desktop ' + 'computer'] + + def variation_count(self) -> int: + return 1 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/toilet_seat_down.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/toilet_seat_down.py new file mode 100644 index 0000000000000000000000000000000000000000..7737f7db1f4b5319aa3e9a0731255930f3198483 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/toilet_seat_down.py @@ -0,0 +1,30 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class ToiletSeatDown(Task): + + def init_task(self) -> None: + self.register_success_conditions([ + JointCondition(Joint('toilet_seat_up_revolute_joint'), 1.7)]) + + def init_episode(self, index: int) -> List[str]: + return ['toilet seat down', + 'put the toilet seat down', + 'grasping the top of the lid, close the toilet seat', + 'put the toilet lid down', + 'leave the toilet seat down', + 'close the lid on the toilet', + 'leave the toilet seat down', + 'lower the toilet seat', + 'grip the edge of the toilet lid and lower it flat on the ' + 'toilet seat'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -np.pi / 4.], [0.0, 0.0, np.pi / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/toilet_seat_up.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/toilet_seat_up.py new file mode 100644 index 0000000000000000000000000000000000000000..aa903f41fd9cbeb221c935d8cbdd184f09682895 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/toilet_seat_up.py @@ -0,0 +1,27 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.joint import Joint +from rlbench.backend.conditions import JointCondition +from rlbench.backend.task import Task + + +class ToiletSeatUp(Task): + + def init_task(self) -> None: + self.register_success_conditions([ + JointCondition(Joint('toilet_seat_up_revolute_joint'), 1.40)]) + + def init_episode(self, index: int) -> List[str]: + return ['lift toilet seat up', + 'put the toilet seat up', + 'leave the lid of the toilet seat in a upright position', + 'grip the edge of the toilet seat and lift it up to an ' + 'upright position', + 'leave the toilet lid open', + 'raise the toilet seat'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -np.pi / 4.], [0.0, 0.0, np.pi / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/turn_oven_on.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/turn_oven_on.py new file mode 100644 index 0000000000000000000000000000000000000000..378dd1b71668c75b8373f850c349c67293f1648e --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/turn_oven_on.py @@ -0,0 +1,32 @@ +from typing import List, Tuple +from pyrep.objects.joint import Joint +from pyrep.objects.shape import Shape +from pyrep.objects.object import Object +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition + + +class TurnOvenOn(Task): + + def init_task(self) -> None: + knob_joint = Joint('oven_knob_joint') + self.register_success_conditions([JointCondition(knob_joint, 1.5)]) + + def init_episode(self, index: int) -> List[str]: + return ['turn on the oven', + 'grip the left most knob on the oven and rotate it anti-' + 'clockwise to turn the oven on', + 'turn the left most knob on the oven to switch the oven on', + 'heat up the oven', + 'get the oven hot', + 'preheat the oven', + 'get the oven heat going'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 4.], [0, 0, 3.14 / 4.] + + def boundary_root(self) -> Object: + return Shape('turn_oven_on_boundary_root') diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/turn_tap.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/turn_tap.py new file mode 100644 index 0000000000000000000000000000000000000000..1a506d0e6570d18481953003f181d0c3055c467f --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/turn_tap.py @@ -0,0 +1,38 @@ +from typing import List +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.conditions import JointCondition + +OPTIONS = ['left', 'right'] + + +class TurnTap(Task): + + def init_task(self) -> None: + self.left_start = Dummy('waypoint0') + self.left_end = Dummy('waypoint1') + self.right_start = Dummy('waypoint5') + self.right_end = Dummy('waypoint6') + self.left_joint = Joint('left_joint') + self.right_joint = Joint('right_joint') + + def init_episode(self, index: int) -> List[str]: + option = OPTIONS[index] + if option == 'right': + self.left_start.set_position(self.right_start.get_position()) + self.left_start.set_orientation(self.right_start.get_orientation()) + self.left_end.set_position(self.right_end.get_position()) + self.left_end.set_orientation(self.right_end.get_orientation()) + self.register_success_conditions( + [JointCondition(self.right_joint, 1.57)]) + else: + self.register_success_conditions( + [JointCondition(self.left_joint, 1.57)]) + + return ['turn %s tap' % option, + 'rotate the %s tap' % option, + 'grasp the %s tap and turn it' % option] + + def variation_count(self) -> int: + return 2 diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/tv_on.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/tv_on.py new file mode 100644 index 0000000000000000000000000000000000000000..3408883274d84c846cbb6b88eacce366303ffb0d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/tv_on.py @@ -0,0 +1,45 @@ +from typing import List, Tuple + +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.shape import Shape +from rlbench.backend.conditions import JointCondition, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +from rlbench.backend.task import Task + + +class TvOn(Task): + + def init_task(self) -> None: + self._remote = Shape('tv_remote') + self.register_graspable_objects([self._remote]) + self.register_success_conditions([ + JointCondition(Joint('target_button_joint0'), 0.003), + DetectedCondition(Dummy('tv_remote_top'), + ProximitySensor('success0')), + DetectedCondition(Dummy('tv_remote_bottom'), + ProximitySensor('success1'))]) + self._spawn_boundary = SpawnBoundary([Shape('spawn_boundary')]) + + def init_episode(self, index: int) -> List[str]: + self._spawn_boundary.clear() + self._spawn_boundary.sample(self._remote) + return [ + 'turn on the TV', + 'point the remote control at the television and turn on the ' + 'television', + 'pick up the remote and rotate it such that the front of the ' + 'remote is pointed straight at the television, then set the ' + 'remote down and press the power button down in order to switch' + ' on the TV', + 'find the power button at the top of the remote, ensure the ' + 'remote is pointed at the tv, then turn the tv on'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + return (0.0, 0.0, -np.pi / 2), (0.0, 0.0, np.pi / 2) diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/unplug_charger.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/unplug_charger.py new file mode 100644 index 0000000000000000000000000000000000000000..c670c1bb15120b846161995609eb5c372f597ded --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/unplug_charger.py @@ -0,0 +1,29 @@ +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped + + +class UnplugCharger(Task): + + def init_task(self) -> None: + charger_success = ProximitySensor('charger_success') + charger = Shape('charger') + self.register_graspable_objects([charger]) + self.register_success_conditions( + [DetectedCondition(charger, charger_success)]) + + def init_episode(self, index: int) -> List[str]: + return ['unplug charger', + 'take the charger out of the wall', + 'grip the black charger and pull it out of its socket', + 'slide the plug out from the wall', + 'remove the charger from the mains', + 'get the charger from the wall plug'] + + def variation_count(self) -> int: + return 1 + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0, 0, -3.14 / 4.], [0, 0, 3.14 / 4.] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/water_plants.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/water_plants.py new file mode 100644 index 0000000000000000000000000000000000000000..d81b356819908504868e4468b3576cd0658fa63c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/water_plants.py @@ -0,0 +1,59 @@ +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.const import PrimitiveShape +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition + +WATER_NUM = 5 + + +class WaterPlants(Task): + + def init_task(self) -> None: + self.drops = [] + self.success_sensor = ProximitySensor('success') + pour_point = ProximitySensor('pour_point') + self.waterer = Shape('waterer') + self.head = Shape('head') + self.register_graspable_objects([self.waterer]) + self.pour_point_reached = DetectedCondition( + self.head, pour_point) + + def init_episode(self, index: int) -> List[str]: + self.register_success_conditions( + [DetectedCondition(self.waterer, self.success_sensor)]) + self.reached = False + self.reachedOnce = False + return ['water plant', + 'pick up the watering can by its handle and water the plant', + 'pour some water on the plant', + 'the plant needs hydration', + 'pour water from the watering can into the plant pot', + 'water the soil'] + + def variation_count(self) -> int: + return 1 + + def step(self) -> None: + if not self.reached: + self.reached = self.pour_point_reached.condition_met()[0] + if self.reached and not self.reachedOnce: + for i in range(WATER_NUM): + drop = Shape.create(PrimitiveShape.CUBOID, mass=0.0001, + size=[0.005, 0.005, 0.005]) + drop.set_color([0.1, 0.1, 0.9]) + pos = list(np.random.normal(0, 0.0005, size=(3,))) + drop.set_position(pos, + relative_to=self.head) + self.drops.append(drop) + self.register_success_conditions( + [DetectedCondition(self.drops[i], self.success_sensor) for i + in range(WATER_NUM)]) + self.reachedOnce = True + + def cleanup(self) -> None: + for d in self.drops: + d.remove() + self.drops = [] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/weighing_scales.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/weighing_scales.py new file mode 100644 index 0000000000000000000000000000000000000000..d239111b5f441bd3655bfe3aa628a9dc7a34042c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/weighing_scales.py @@ -0,0 +1,94 @@ +from typing import List, Tuple +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.joint import Joint +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition, NothingGrasped +from rlbench.backend.spawn_boundary import SpawnBoundary + +UNIQUE_PEPPERS_TO_PLACE = 3 + + +class WeighingScales(Task): + + def init_task(self) -> None: + self.needle = Shape('scales_meter_needle') + self.needle_pivot = Shape('scales_meter_pivot') + self.top_plate = Shape('scales_tray_visual') + self.joint = Joint('scales_joint') + + _, _, starting_z = self.top_plate.get_position() + self.top_plate_starting_z = starting_z + self.needle_starting_ori = self.needle.get_orientation( + relative_to=self.needle_pivot) + + self.peppers = [Shape('pepper%d' % i) for i in range(3)] + self.register_graspable_objects(self.peppers) + + self.boundary = Shape('peppers_boundary') + + self.success_detector = ProximitySensor('success_detector') + self.needle_detector = ProximitySensor('needle_sensor') + self.success_conditions = [NothingGrasped(self.robot.gripper), + DetectedCondition( + self.needle, self.needle_detector) + ] + + self.w0 = Dummy('waypoint0') + self.w0_rel_pos = [+2.6203 * 10 ** (-3), -1.7881 * 10 ** (-7), + +1.5197 * 10 ** (-1)] + self.w0_rel_ori = [-3.1416, -1.7467 * 10 ** (-2), -3.1416] + + def init_episode(self, index: int) -> List[str]: + self.target_pepper_index = index + while len(self.success_conditions) > 2: + self.success_conditions.pop() + self.success_conditions.append(DetectedCondition( + self.peppers[self.target_pepper_index], self.success_detector)) + self.register_success_conditions(self.success_conditions) + b = SpawnBoundary([self.boundary]) + for p in self.peppers: + b.sample(p, ignore_collisions=False, min_distance=0.12, + min_rotation=(0.00, 0.00, -3.14), + max_rotation=(0.00, 0.00, +3.14) + ) + + self.w0.set_position(self.w0_rel_pos, + relative_to=self.peppers[ + self.target_pepper_index], + reset_dynamics=False) + self.w0.set_orientation(self.w0_rel_ori, + relative_to=self.peppers[ + self.target_pepper_index], + reset_dynamics=False) + pepper = {0: 'green', 1: 'red', 2: 'yellow'} + + return ['weigh the %s pepper' % pepper[index], + 'pick up the %s pepper and set it down on the weighing scales' + % pepper[index], + 'put the %s pepper on the scales' % pepper[index], + 'place the %s pepper onto the scales tray' % pepper[index], + 'lift up the %s pepper onto the weighing scales' + % pepper[index], + 'grasp the %s pepper from above and lower it onto the tray' + % pepper[index]] + + def variation_count(self) -> int: + return UNIQUE_PEPPERS_TO_PLACE + + def step(self): + _, _, pos_z = self.top_plate.get_position() + dz = self.top_plate_starting_z - pos_z + d_alpha = -120 * dz + new_needle_ori = [self.needle_starting_ori[0] + d_alpha, + self.needle_starting_ori[1], + self.needle_starting_ori[2]] + + self.needle.set_orientation(new_needle_ori, + relative_to=self.needle_pivot, + reset_dynamics=False) + + def base_rotation_bounds(self) -> Tuple[List[float], List[float]]: + return [0.0, 0.0, -0.25 * np.pi], [0.0, 0.0, +0.25 * np.pi] diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/wipe_desk.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/wipe_desk.py new file mode 100644 index 0000000000000000000000000000000000000000..2a000c05dcdfc7c54c67178250ac0272dc6f20f0 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/tasks/wipe_desk.py @@ -0,0 +1,62 @@ +from typing import List +from pyrep.const import PrimitiveShape +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.backend.task import Task +from rlbench.backend.conditions import EmptyCondition +from rlbench.backend.spawn_boundary import SpawnBoundary + +DIRT_POINTS = 50 + + +class WipeDesk(Task): + + def init_task(self) -> None: + self.dirt_spots = [] + self.sponge = Shape('sponge') + self.sensor = ProximitySensor('sponge_sensor') + self.register_graspable_objects([self.sponge]) + + boundaries = [Shape('dirt_boundary')] + _, _, self.z_boundary = boundaries[0].get_position() + self.b = SpawnBoundary(boundaries) + + def init_episode(self, index: int) -> List[str]: + self._place_dirt() + self.register_success_conditions([EmptyCondition(self.dirt_spots)]) + return ['wipe dirt off the desk', + 'use the sponge to clean up the desk', + 'remove the dirt from the desk', + 'grip the sponge and wipe it back and forth over any dirt you ' + 'see', + 'clean up the mess', + 'wipe the dirt up'] + + def variation_count(self) -> int: + return 1 + + def step(self) -> None: + for d in self.dirt_spots: + if self.sensor.is_detected(d): + self.dirt_spots.remove(d) + d.remove() + + def cleanup(self) -> None: + for d in self.dirt_spots: + d.remove() + self.dirt_spots = [] + + def _place_dirt(self): + for i in range(DIRT_POINTS): + spot = Shape.create(type=PrimitiveShape.CUBOID, + size=[.005, .005, .001], + mass=0, static=True, respondable=False, + renderable=True, + color=[0.58, 0.29, 0.0]) + spot.set_parent(self.get_base()) + spot.set_position([-1, -1, self.z_boundary + 0.001]) + self.b.sample(spot, min_distance=0.00, + min_rotation=(0.00, 0.00, 0.00), + max_rotation=(0.00, 0.00, 0.00)) + self.dirt_spots.append(spot) + self.b.clear() diff --git a/third_party/AnyBimanual/third_party/RLBench/rlbench/utils.py b/third_party/AnyBimanual/third_party/RLBench/rlbench/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2b43c4f9ff19e1bc554bcf9511376dc49581aa40 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/rlbench/utils.py @@ -0,0 +1,201 @@ +import importlib +import pickle + +from itertools import product +import os +from os import listdir +from os.path import join, exists +from typing import List + +import numpy as np +from PIL import Image +from natsort import natsorted +from pyrep.objects import VisionSensor + +from rlbench.backend.const import * +from rlbench.backend.utils import image_to_float_array, rgb_handles_to_mask +from rlbench.demo import Demo +from rlbench.observation_config import ObservationConfig + +import logging + + +class InvalidTaskName(Exception): + pass + + +def name_to_task_class(task_file: str, bimanual=False): + name = task_file.replace('.py', '') + class_name = ''.join([w[0].upper() + w[1:] for w in name.split('_')]) + try: + if bimanual: + mod = importlib.import_module("rlbench.bimanual_tasks.%s" % name) + else: + mod = importlib.import_module("rlbench.tasks.%s" % name) + mod = importlib.reload(mod) + except ModuleNotFoundError as e: + raise InvalidTaskName( + "The task file '%s' does not exist or cannot be compiled." + % name) from e + try: + task_class = getattr(mod, class_name) + except AttributeError as e: + raise InvalidTaskName( + "Cannot find the class name '%s' in the file '%s'." + % (class_name, name)) from e + return task_class + + +def get_stored_demos(amount: int, image_paths: bool, dataset_root: str, + variation_number: int, task_name: str, + obs_config: ObservationConfig, + random_selection: bool = True, + from_episode_number: int = 0) -> List[Demo]: + + task_root = join(dataset_root, task_name) + if not exists(task_root): + raise RuntimeError("Can't find the demos for %s at: %s" % ( + task_name, task_root)) + + if variation_number == -1: + # All variations + examples_path = join( + task_root, VARIATIONS_ALL_FOLDER, + EPISODES_FOLDER) + examples = listdir(examples_path) + else: + # Sample an amount of examples for the variation of this task + examples_path = join( + task_root, VARIATIONS_FOLDER % variation_number, + EPISODES_FOLDER) + examples = listdir(examples_path) + + # hack: ignore .DS_Store files from macOS zips + examples = [e for e in examples if '.DS_Store' not in e] + + if amount == -1: + amount = len(examples) + if amount > len(examples): + raise RuntimeError( + 'You asked for %d examples, but only %d were available.' % ( + amount, len(examples))) + + if amount > len(examples[from_episode_number:]): + raise RuntimeError('You specified from_episode_number=%d, but only %d examples were available', from_episode_number, len(examples)) + + if random_selection: + selected_examples = np.random.choice(examples, amount, replace=False) + else: + selected_examples = natsorted( + examples)[from_episode_number:from_episode_number+amount] + + # Process these examples (e.g. loading observations) + demos = [] + for example in selected_examples: + example_path = join(examples_path, example) + with open(join(example_path, LOW_DIM_PICKLE), 'rb') as f: + obs = pickle.load(f) + + if variation_number == -1: + with open(join(example_path, VARIATION_NUMBER), 'rb') as f: + obs.variation_number = pickle.load(f) + else: + obs.variation_number = variation_number + + # language description + episode_descriptions_f = join(example_path, VARIATION_DESCRIPTIONS) + if exists(episode_descriptions_f): + with open(episode_descriptions_f, 'rb') as f: + descriptions = pickle.load(f) + else: + descriptions = ["unknown task description"] + + num_steps = len(obs) + + camera_names = obs_config.camera_configs.keys() + + data_types = ["rgb", "depth", "mask"] + full_camera_names = map(lambda x: ('_'.join(x), x[-1]), product(camera_names, data_types)) + + for camera_name, _ in full_camera_names: + data_path = os.path.join(example_path, camera_name) + if num_steps != len(os.listdir(data_path)): + print(f"not sufficent data points {data_path} expected {num_steps} was {len(os.listdir(data_path))}") + #raise RuntimeError('Broken dataset assumption') + + for i in range(num_steps): + # descriptions + obs[i].misc['descriptions'] = descriptions + + for camera_name, camera_config in obs_config.camera_configs.items(): + + if camera_config.rgb: + data_path = os.path.join(example_path, f"{camera_name}_rgb") + image_name = f"rgb_{i:04d}.png" + image_path = os.path.join(data_path, image_name) + image = np.array(_resize_if_needed(Image.open(image_path), camera_config.image_size)) + obs[i].perception_data[f"{camera_name}_rgb"] = image + + if camera_config.depth or camera_config.point_cloud: + data_path = os.path.join(example_path, f"{camera_name}_depth") + image_name = f"depth_{i:04d}.png" + image_path = os.path.join(data_path, image_name) + image = image_to_float_array( _resize_if_needed(Image.open(image_path), camera_config.image_size),DEPTH_SCALE) + + if camera_config.depth: + if camera_config.depth_in_meters: + near = obs[i].misc[f'{camera_name}_camera_near'] + far = obs[i].misc[f'{camera_name}_camera_far'] + depth_image_m = near + image * (far - near) + obs[i].perception_data[f"{camera_name}_depth"] = camera_config.depth_noise.apply(depth_image_m) + else: + obs[i].perception_data[f"{camera_name}_depth"] = camera_config.depth_noise.apply(image) + + if camera_config.point_cloud: + # print("point_cloud") + near = obs[i].misc[f'{camera_name}_camera_near'] + far = obs[i].misc[f'{camera_name}_camera_far'] + # far = 1.5 # real + depth_image_m = near + image * (far - near) + + obs[i].perception_data[f"{camera_name}_point_cloud"] = VisionSensor.pointcloud_from_depth_and_camera_params( + depth_image_m, + obs[i].misc[f'{camera_name}_camera_extrinsics'], + obs[i].misc[f'{camera_name}_camera_intrinsics']) + + + if camera_config.mask: + data_path = os.path.join(example_path, f"{camera_name}_mask") + image_name = f"mask_{i:04d}.png" + image_path = os.path.join(data_path, image_name) + obs[i].perception_data[f"{camera_name}_mask"] = rgb_handles_to_mask(np.array(_resize_if_needed(Image.open(image_path), camera_config.image_size))) + + + # Remove low dim info if necessary + if not obs_config.joint_velocities: + obs[i].joint_velocities = None + if not obs_config.joint_positions: + obs[i].joint_positions = None + if not obs_config.joint_forces: + obs[i].joint_forces = None + if not obs_config.gripper_open: + obs[i].gripper_open = None + if not obs_config.gripper_pose: + obs[i].gripper_pose = None + if not obs_config.gripper_joint_positions: + obs[i].gripper_joint_positions = None + if not obs_config.gripper_touch_forces: + obs[i].gripper_touch_forces = None + if not obs_config.task_low_dim_state: + obs[i].task_low_dim_state = None + + + + demos.append(obs) + return demos + + +def _resize_if_needed(image, size): + if image.size[0] != size[0] or image.size[1] != size[1]: + image = image.resize(size) + return image diff --git a/third_party/AnyBimanual/third_party/RLBench/tests/__init__.py b/third_party/AnyBimanual/third_party/RLBench/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/RLBench/tests/demos/__init__.py b/third_party/AnyBimanual/third_party/RLBench/tests/demos/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/RLBench/tests/demos/test_demos.py b/third_party/AnyBimanual/third_party/RLBench/tests/demos/test_demos.py new file mode 100644 index 0000000000000000000000000000000000000000..4d6ed63b2ef7d93ed1670b278fb10fd1d8f0180d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tests/demos/test_demos.py @@ -0,0 +1,63 @@ +import unittest +import rlbench.backend.task as task +import os +from rlbench.backend.utils import task_file_to_task_class +from pyrep import PyRep +from pyrep.robots.arms.panda import Panda +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from rlbench.backend.const import TTT_FILE +from tools.task_validator import task_smoke +from rlbench.observation_config import ObservationConfig +from rlbench.backend.scene import Scene +from rlbench.backend.robot import Robot +from rlbench.backend.robot import UnimanualRobot + + +TASKS = [t for t in os.listdir(task.TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] +DIR_PATH = os.path.dirname(os.path.abspath(__file__)) + +# Task does work, but fails demos often. These should eventually be improved. +FLAKY_TASKS = ['put_all_groceries_in_cupboard', 'take_cup_out_from_cabinet', + 'slide_cabinet_open_and_place_cups', 'slide_cabinet_open'] + + +class TestTasks(unittest.TestCase): + """Tests all of the tasks via the task_validator tool. + + Given that unit tests shouldn't take forever to run, we only limit + each validation run to 1 variation. In practice, a newly created task + should be validated for all variations. Despite this, the test still takes + a while to run. + """ + + def test_run_task_validator(self): + for task_file in TASKS: + test_name = task_file.split('.py')[0] + with self.subTest(task=test_name): + if test_name in FLAKY_TASKS: + self.skipTest('Flaky task.') + sim = PyRep() + ttt_file = os.path.join( + DIR_PATH, '..', '..', 'rlbench', TTT_FILE) + sim.launch(ttt_file, headless=True) + sim.step_ui() + sim.set_simulation_timestep(50.0) + sim.step_ui() + sim.start() + robot = UnimanualRobot(Panda(), PandaGripper()) + obs = ObservationConfig() + obs.set_all(False) + scene = Scene(sim, robot, obs) + sim.start() + task_class = task_file_to_task_class(task_file) + active_task = task_class(sim, robot) + try: + task_smoke(active_task, scene, variation=-1, + max_variations=2, success=0.25) + except Exception as e: + sim.stop() + sim.shutdown() + raise e + sim.stop() + sim.shutdown() diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/assets/bimanual_task_template.txt b/third_party/AnyBimanual/third_party/RLBench/tools/assets/bimanual_task_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..b87d5c0492667d9754e98423211599352bf75af5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/assets/bimanual_task_template.txt @@ -0,0 +1,26 @@ +from typing import List +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask + + +class %s(BimanualTask): + + def init_task(self) -> None: + # TODO: This is called once when a task is initialised. + pass + + def init_episode(self, index: int) -> List[str]: + # TODO: This is called at the start of each episode. + return [''] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 1 + + def step(self) -> None: + # Called during each sim step. Remove this if not using. + pass + + def cleanup(self) -> None: + # Called during at the end of each episode. Remove this if not using. + pass diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/assets/task_template.txt b/third_party/AnyBimanual/third_party/RLBench/tools/assets/task_template.txt new file mode 100644 index 0000000000000000000000000000000000000000..4cc8c4d0736ce63b39f45e876098fdc0e1130070 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/assets/task_template.txt @@ -0,0 +1,25 @@ +from typing import List +from rlbench.backend.task import Task + + +class %s(Task): + + def init_task(self) -> None: + # TODO: This is called once when a task is initialised. + pass + + def init_episode(self, index: int) -> List[str]: + # TODO: This is called at the start of each episode. + return [''] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 1 + + def step(self) -> None: + # Called during each sim step. Remove this if not using. + pass + + def cleanup(self) -> None: + # Called during at the end of each episode. Remove this if not using. + pass diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/cinematic_recorder.py b/third_party/AnyBimanual/third_party/RLBench/tools/cinematic_recorder.py new file mode 100644 index 0000000000000000000000000000000000000000..fed238c77efd7c05f6c526ea3ff30302c5a9914c --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/cinematic_recorder.py @@ -0,0 +1,158 @@ +import os +from typing import Type +import numpy as np +from absl import app +from absl import flags +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +from rlbench import Environment +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.backend.observation import Observation +from rlbench.backend.task import TASKS_PATH +from rlbench.backend.task import Task +from rlbench.backend.utils import task_file_to_task_class +from rlbench.observation_config import ObservationConfig +from rlbench.sim2real.domain_randomization import RandomizeEvery, \ + VisualRandomizationConfig + +FLAGS = flags.FLAGS + +flags.DEFINE_string( + 'save_dir', '/tmp/rlbench_videos/', + 'Where to save the generated videos.') +flags.DEFINE_list( + 'tasks', [], 'The tasks to record. If empty, all tasks are recorded.') +flags.DEFINE_boolean( + 'individual', True, 'One long clip of all the tasks, or individual videos.') +flags.DEFINE_boolean( + 'domain_randomization', False, 'If domain randomization should be applied.') +flags.DEFINE_string( + 'textures_path', '', + 'Where to locate textures if using domain randomization.') +flags.DEFINE_boolean('headless', True, 'Run in headless mode.') +flags.DEFINE_list( + 'camera_resolution', [1280, 720], 'The camera resolution') + + +class CameraMotion(object): + def __init__(self, cam: VisionSensor): + self.cam = cam + + def step(self): + raise NotImplementedError() + + def save_pose(self): + self._prev_pose = self.cam.get_pose() + + def restore_pose(self): + self.cam.set_pose(self._prev_pose) + + +class CircleCameraMotion(CameraMotion): + + def __init__(self, cam: VisionSensor, origin: Dummy, + speed: float, init_rotation: float = np.deg2rad(0)): + super().__init__(cam) + self.origin = origin + self.speed = speed # in radians + self.origin.rotate([0, 0, init_rotation]) + + def step(self): + self.origin.rotate([0, 0, self.speed]) + + +class TaskRecorder(object): + + def __init__(self, env: Environment, cam_motion: CameraMotion, fps=30): + self._env = env + self._cam_motion = cam_motion + self._fps = fps + self._snaps = [] + self._current_snaps = [] + + def take_snap(self, obs: Observation): + self._cam_motion.step() + self._current_snaps.append( + (self._cam_motion.cam.capture_rgb() * 255.).astype(np.uint8)) + + def record_task(self, task: Type[Task]): + task = self._env.get_task(task) + self._cam_motion.save_pose() + while True: + try: + task.get_demos( + 1, live_demos=True, callable_each_step=self.take_snap, + max_attempts=1) + break + except RuntimeError: + self._cam_motion.restore_pose() + self._current_snaps = [] + self._snaps.extend(self._current_snaps) + self._current_snaps = [] + return True + + def save(self, path): + print('Converting to video ...') + os.makedirs(os.path.dirname(path), exist_ok=True) + # OpenCV QT version can conflict with PyRep, so import here + import cv2 + video = cv2.VideoWriter( + path, cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), self._fps, + tuple(self._cam_motion.cam.get_resolution())) + for image in self._snaps: + video.write(cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) + video.release() + self._snaps = [] + + +def main(argv): + + obs_config = ObservationConfig(record_gripper_closing=True) + obs_config.set_all(False) + + vrc = rand_every = None + frequency = 0 + if FLAGS.domain_randomization: + vrc = VisualRandomizationConfig(FLAGS.textures_path) + rand_every = RandomizeEvery.TRANSITION + frequency = 10 + + action_mode = MoveArmThenGripper( + arm_action_mode=JointVelocity(), gripper_action_mode=Discrete()) + env = Environment(action_mode, obs_config=obs_config, + randomize_every=rand_every, frequency=frequency, + visual_randomization_config=vrc, headless=FLAGS.headless) + env.launch() + + # Add the camera to the scene + cam_placeholder = Dummy('cam_cinematic_placeholder') + cam = VisionSensor.create(FLAGS.camera_resolution) + cam.set_pose(cam_placeholder.get_pose()) + cam.set_parent(cam_placeholder) + + cam_motion = CircleCameraMotion(cam, Dummy('cam_cinematic_base'), 0.005) + tr = TaskRecorder(env, cam_motion, fps=30) + + if len(FLAGS.tasks) > 0: + task_names = FLAGS.tasks + else: + task_names = [t.split('.py')[0] for t in os.listdir(TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + task_classes = [task_file_to_task_class( + task_file) for task_file in task_names] + + for i, (name, cls) in enumerate(zip(task_names, task_classes)): + good = tr.record_task(cls) + if FLAGS.individual and good: + tr.save(os.path.join(FLAGS.save_dir, '%s.avi' % name)) + + if not FLAGS.individual: + tr.save(os.path.join(FLAGS.save_dir, 'recorded_tasks.mp4')) + env.shutdown() + + +if __name__ == '__main__': + app.run(main) diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/create_videos.py b/third_party/AnyBimanual/third_party/RLBench/tools/create_videos.py new file mode 100644 index 0000000000000000000000000000000000000000..55752cee56eb188cbdf469a345cfb23496ce67ba --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/create_videos.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 + + +import os +import traceback + +from pyrep import PyRep +from pyrep.const import RenderMode +from pyrep.robots.arms.dual_panda import PandaLeft, PandaRight +from pyrep.robots.end_effectors.dual_panda_gripper import PandaGripperLeft, PandaGripperRight +from pyrep.objects.shape import Shape + +from yarr.utils.video_utils import CircleCameraMotion +from yarr.utils.video_utils import TaskRecorder + +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +from rlbench.backend.exceptions import * +from rlbench.observation_config import ObservationConfig +from rlbench.observation_config import CameraConfig + +from rlbench.backend.robot import Robot +from rlbench.backend.robot import BimanualRobot +from rlbench.backend.scene import Scene + +from rlbench.backend.task import BIMANUAL_TASKS_PATH +from rlbench.backend.const import BIMANUAL_TTT_FILE + +import rich_click as click +from click_prompt import choice_option + + +from rlbench.utils import name_to_task_class + +def main(): + render_videos_for_task() + + +def get_bimanual_tasks(): + return [t.replace('.py', '') for t in + os.listdir(BIMANUAL_TASKS_PATH) if t != '__init__.py' and t.endswith('.py')] + + +@click.command() +@choice_option('--bimanual-task-files', type=click.Choice(sorted(get_bimanual_tasks())), multiple=True) +@click.option('--add-task-description/--no-task-description', default=False) +@click.option('--add-gripper-color/--no-gripper-color', default=False) +def render_videos_for_task(bimanual_task_files, add_task_description, add_gripper_color): + + CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) + + pr = PyRep() + ttt_file = os.path.join(CURRENT_DIR, '..', 'rlbench', BIMANUAL_TTT_FILE) + pr.launch(ttt_file, responsive_ui=True) + pr.step_ui() + + robot = BimanualRobot(PandaRight(), PandaGripperRight(), PandaLeft(), PandaGripperLeft()) + + if add_gripper_color: + robot.colorize_gripper() + + cam_config = CameraConfig(rgb=True, depth=False, mask=False, + render_mode=RenderMode.OPENGL3) + obs_config = ObservationConfig() + obs_config.set_all(False) + obs_config.right_shoulder_camera = cam_config + obs_config.left_shoulder_camera = cam_config + obs_config.overhead_camera = cam_config + obs_config.wrist_camera = cam_config + obs_config.front_camera = cam_config + + scene = Scene(pr, robot, obs_config) + + for task_file in bimanual_task_files: + + + if add_task_description: + task_name_mapping = {} + task_description = task_name_mapping.get(task_file, f"Demo for task {task_file}") + else: + task_description = "" + + task_class = name_to_task_class(task_file, True) + task = task_class(pr, robot, task_file.replace('.py', '')) + try: + print(f"Loading {task_file}") + scene.load(task) + pr.start() + pr.step_ui() + + record_demo(scene, task, task_file, task_description) + scene.reset() + scene.unload() + + pr.stop() + pr.step_ui() + + + except Exception as e: + print("error while loading task") + print(e) + + + pr.step_ui() + + pr.stop() + pr.shutdown() + + +def record_demo(scene, task, task_file, task_description): + + cam_placeholder = Dummy('cam_cinematic_placeholder') + cam = VisionSensor.create([1920, 1200], background_color=[1.0, 1.0, 1.0]) + cam.set_pose(cam_placeholder.get_pose()) + cam.set_parent(cam_placeholder) + + scene_background = ["Wall1", "Wall2", "Wall3", "Wall4", "Floor", "Roof", "ResizableFloor_5_25_visibleElement"] + + for object_name in scene_background: + Shape(object_name).set_renderable(False) + + cam_motion = CircleCameraMotion(cam, Dummy('cam_cinematic_base'), 0.005) + tr = TaskRecorder(None, cam_motion, fps=30) + + try: + scene.get_demo(False, callable_each_step=tr.take_snap, randomly_place=False) + except (WaypointError, NoWaypointsError, DemoError, Exception) as e: + traceback.print_exc() + return + success, terminate = task.success() + + task_result = "success" if success else "failed" + + recording_output_path = f"/tmp/{task_result}/rlbench_video_{task_file}.mp4" + + os.makedirs(os.path.dirname(recording_output_path), exist_ok=True) + + tr.save(recording_output_path, task_description, None) + + print(f"Saving video to {recording_output_path}") + + for object_name in scene_background: + Shape(object_name).set_renderable(True) + + if success: + print("Demo was a success!") + + +if __name__ == '__main__': + main() diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/dataset_generator.py b/third_party/AnyBimanual/third_party/RLBench/tools/dataset_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..a7ffc511524fb92eec5cd8ea06a594c1d2ceeacd --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/dataset_generator.py @@ -0,0 +1,461 @@ +from multiprocessing import Process, Manager + +from pyrep.const import RenderMode + +from rlbench import ObservationConfig +from rlbench.action_modes.action_mode import MoveArmThenGripper +from rlbench.action_modes.arm_action_modes import JointVelocity +from rlbench.action_modes.gripper_action_modes import Discrete +from rlbench.backend.utils import task_file_to_task_class +from rlbench.environment import Environment +import rlbench.backend.task as task + +import os +import pickle +from PIL import Image +from rlbench.backend import utils +from rlbench.backend.const import * +import numpy as np + +from absl import app +from absl import flags + +FLAGS = flags.FLAGS + +flags.DEFINE_string('save_path', + '/tmp/rlbench_data/', + 'Where to save the demos.') +flags.DEFINE_list('tasks', [], + 'The tasks to collect. If empty, all tasks are collected.') +flags.DEFINE_list('image_size', [128, 128], + 'The size of the images tp save.') +flags.DEFINE_enum('renderer', 'opengl3', ['opengl', 'opengl3'], + 'The renderer to use. opengl does not include shadows, ' + 'but is faster.') +flags.DEFINE_integer('processes', 1, + 'The number of parallel processes during collection.') +flags.DEFINE_integer('episodes_per_task', 10, + 'The number of episodes to collect per task.') +flags.DEFINE_integer('variations', -1, + 'Number of variations to collect per task. -1 for all.') +flags.DEFINE_bool('all_variations', True, + 'Include all variations when sampling epsiodes') + + +def check_and_make(dir): + if not os.path.exists(dir): + os.makedirs(dir) + + +def save_demo(demo, example_path, variation): + + # Save image data first, and then None the image data, and pickle + left_shoulder_rgb_path = os.path.join( + example_path, LEFT_SHOULDER_RGB_FOLDER) + left_shoulder_depth_path = os.path.join( + example_path, LEFT_SHOULDER_DEPTH_FOLDER) + left_shoulder_mask_path = os.path.join( + example_path, LEFT_SHOULDER_MASK_FOLDER) + right_shoulder_rgb_path = os.path.join( + example_path, RIGHT_SHOULDER_RGB_FOLDER) + right_shoulder_depth_path = os.path.join( + example_path, RIGHT_SHOULDER_DEPTH_FOLDER) + right_shoulder_mask_path = os.path.join( + example_path, RIGHT_SHOULDER_MASK_FOLDER) + overhead_rgb_path = os.path.join( + example_path, OVERHEAD_RGB_FOLDER) + overhead_depth_path = os.path.join( + example_path, OVERHEAD_DEPTH_FOLDER) + overhead_mask_path = os.path.join( + example_path, OVERHEAD_MASK_FOLDER) + wrist_rgb_path = os.path.join(example_path, WRIST_RGB_FOLDER) + wrist_depth_path = os.path.join(example_path, WRIST_DEPTH_FOLDER) + wrist_mask_path = os.path.join(example_path, WRIST_MASK_FOLDER) + front_rgb_path = os.path.join(example_path, FRONT_RGB_FOLDER) + front_depth_path = os.path.join(example_path, FRONT_DEPTH_FOLDER) + front_mask_path = os.path.join(example_path, FRONT_MASK_FOLDER) + + check_and_make(left_shoulder_rgb_path) + check_and_make(left_shoulder_depth_path) + check_and_make(left_shoulder_mask_path) + check_and_make(right_shoulder_rgb_path) + check_and_make(right_shoulder_depth_path) + check_and_make(right_shoulder_mask_path) + check_and_make(overhead_rgb_path) + check_and_make(overhead_depth_path) + check_and_make(overhead_mask_path) + check_and_make(wrist_rgb_path) + check_and_make(wrist_depth_path) + check_and_make(wrist_mask_path) + check_and_make(front_rgb_path) + check_and_make(front_depth_path) + check_and_make(front_mask_path) + + for i, obs in enumerate(demo): + left_shoulder_rgb = Image.fromarray(obs.left_shoulder_rgb) + left_shoulder_depth = utils.float_array_to_rgb_image( + obs.left_shoulder_depth, scale_factor=DEPTH_SCALE) + left_shoulder_mask = Image.fromarray( + (obs.left_shoulder_mask * 255).astype(np.uint8)) + right_shoulder_rgb = Image.fromarray(obs.right_shoulder_rgb) + right_shoulder_depth = utils.float_array_to_rgb_image( + obs.right_shoulder_depth, scale_factor=DEPTH_SCALE) + right_shoulder_mask = Image.fromarray( + (obs.right_shoulder_mask * 255).astype(np.uint8)) + overhead_rgb = Image.fromarray(obs.overhead_rgb) + overhead_depth = utils.float_array_to_rgb_image( + obs.overhead_depth, scale_factor=DEPTH_SCALE) + overhead_mask = Image.fromarray( + (obs.overhead_mask * 255).astype(np.uint8)) + wrist_rgb = Image.fromarray(obs.wrist_rgb) + wrist_depth = utils.float_array_to_rgb_image( + obs.wrist_depth, scale_factor=DEPTH_SCALE) + wrist_mask = Image.fromarray((obs.wrist_mask * 255).astype(np.uint8)) + front_rgb = Image.fromarray(obs.front_rgb) + front_depth = utils.float_array_to_rgb_image( + obs.front_depth, scale_factor=DEPTH_SCALE) + front_mask = Image.fromarray((obs.front_mask * 255).astype(np.uint8)) + + left_shoulder_rgb.save( + os.path.join(left_shoulder_rgb_path, IMAGE_FORMAT % i)) + left_shoulder_depth.save( + os.path.join(left_shoulder_depth_path, IMAGE_FORMAT % i)) + left_shoulder_mask.save( + os.path.join(left_shoulder_mask_path, IMAGE_FORMAT % i)) + right_shoulder_rgb.save( + os.path.join(right_shoulder_rgb_path, IMAGE_FORMAT % i)) + right_shoulder_depth.save( + os.path.join(right_shoulder_depth_path, IMAGE_FORMAT % i)) + right_shoulder_mask.save( + os.path.join(right_shoulder_mask_path, IMAGE_FORMAT % i)) + overhead_rgb.save( + os.path.join(overhead_rgb_path, IMAGE_FORMAT % i)) + overhead_depth.save( + os.path.join(overhead_depth_path, IMAGE_FORMAT % i)) + overhead_mask.save( + os.path.join(overhead_mask_path, IMAGE_FORMAT % i)) + wrist_rgb.save(os.path.join(wrist_rgb_path, IMAGE_FORMAT % i)) + wrist_depth.save(os.path.join(wrist_depth_path, IMAGE_FORMAT % i)) + wrist_mask.save(os.path.join(wrist_mask_path, IMAGE_FORMAT % i)) + front_rgb.save(os.path.join(front_rgb_path, IMAGE_FORMAT % i)) + front_depth.save(os.path.join(front_depth_path, IMAGE_FORMAT % i)) + front_mask.save(os.path.join(front_mask_path, IMAGE_FORMAT % i)) + + # We save the images separately, so set these to None for pickling. + obs.left_shoulder_rgb = None + obs.left_shoulder_depth = None + obs.left_shoulder_point_cloud = None + obs.left_shoulder_mask = None + obs.right_shoulder_rgb = None + obs.right_shoulder_depth = None + obs.right_shoulder_point_cloud = None + obs.right_shoulder_mask = None + obs.overhead_rgb = None + obs.overhead_depth = None + obs.overhead_point_cloud = None + obs.overhead_mask = None + obs.wrist_rgb = None + obs.wrist_depth = None + obs.wrist_point_cloud = None + obs.wrist_mask = None + obs.front_rgb = None + obs.front_depth = None + obs.front_point_cloud = None + obs.front_mask = None + + # Save the low-dimension data + with open(os.path.join(example_path, LOW_DIM_PICKLE), 'wb') as f: + pickle.dump(demo, f) + + + with open(os.path.join(example_path, VARIATION_NUMBER), 'wb') as f: + pickle.dump(variation, f) + + +def run(i, lock, task_index, variation_count, results, file_lock, tasks): + """Each thread will choose one task and variation, and then gather + all the episodes_per_task for that variation.""" + + # Initialise each thread with random seed + np.random.seed(None) + num_tasks = len(tasks) + + img_size = list(map(int, FLAGS.image_size)) + + obs_config = ObservationConfig() + obs_config.set_all(True) + obs_config.right_shoulder_camera.image_size = img_size + obs_config.left_shoulder_camera.image_size = img_size + obs_config.overhead_camera.image_size = img_size + obs_config.wrist_camera.image_size = img_size + obs_config.front_camera.image_size = img_size + + # Store depth as 0 - 1 + obs_config.right_shoulder_camera.depth_in_meters = False + obs_config.left_shoulder_camera.depth_in_meters = False + obs_config.overhead_camera.depth_in_meters = False + obs_config.wrist_camera.depth_in_meters = False + obs_config.front_camera.depth_in_meters = False + + # We want to save the masks as rgb encodings. + obs_config.left_shoulder_camera.masks_as_one_channel = False + obs_config.right_shoulder_camera.masks_as_one_channel = False + obs_config.overhead_camera.masks_as_one_channel = False + obs_config.wrist_camera.masks_as_one_channel = False + obs_config.front_camera.masks_as_one_channel = False + + if FLAGS.renderer == 'opengl': + obs_config.right_shoulder_camera.render_mode = RenderMode.OPENGL + obs_config.left_shoulder_camera.render_mode = RenderMode.OPENGL + obs_config.overhead_camera.render_mode = RenderMode.OPENGL + obs_config.wrist_camera.render_mode = RenderMode.OPENGL + obs_config.front_camera.render_mode = RenderMode.OPENGL + + rlbench_env = Environment( + action_mode=MoveArmThenGripper(JointVelocity(), Discrete()), + obs_config=obs_config, + headless=True) + rlbench_env.launch() + + task_env = None + + tasks_with_problems = results[i] = '' + + while True: + # Figure out what task/variation this thread is going to do + with lock: + + if task_index.value >= num_tasks: + print('Process', i, 'finished') + break + + my_variation_count = variation_count.value + t = tasks[task_index.value] + task_env = rlbench_env.get_task(t) + var_target = task_env.variation_count() + if FLAGS.variations >= 0: + var_target = np.minimum(FLAGS.variations, var_target) + if my_variation_count >= var_target: + # If we have reached the required number of variations for this + # task, then move on to the next task. + variation_count.value = my_variation_count = 0 + task_index.value += 1 + + variation_count.value += 1 + if task_index.value >= num_tasks: + print('Process', i, 'finished') + break + t = tasks[task_index.value] + + variation_path = os.path.join( + FLAGS.save_path, task_env.get_name(), + VARIATIONS_FOLDER % my_variation_count) + check_and_make(variation_path) + + episodes_path = os.path.join(variation_path, EPISODES_FOLDER) + check_and_make(episodes_path) + + abort_variation = False + for ex_idx in range(FLAGS.episodes_per_task): + print('Process', i, '// Task:', task_env.get_name(), + '// Variation:', my_variation_count, '// Demo:', ex_idx) + attempts = 10 + while attempts > 0: + try: + task_env = rlbench_env.get_task(t) + task_env.set_variation(my_variation_count) + descriptions, obs = task_env.reset() + + # TODO: for now we do the explicit looping. + demo, = task_env.get_demos( + amount=1, + live_demos=True) + except Exception as e: + attempts -= 1 + if attempts > 0: + continue + problem = ( + 'Process %d failed collecting task %s (variation: %d, ' + 'example: %d). Skipping this task/variation.\n%s\n' % ( + i, task_env.get_name(), my_variation_count, ex_idx, + str(e)) + ) + print(problem) + tasks_with_problems += problem + abort_variation = True + break + episode_path = os.path.join(episodes_path, EPISODE_FOLDER % ex_idx) + with file_lock: + save_demo(demo, episode_path, my_variation_count) + + with open(os.path.join( + episode_path, VARIATION_DESCRIPTIONS), 'wb') as f: + pickle.dump(descriptions, f) + break + if abort_variation: + break + + results[i] = tasks_with_problems + rlbench_env.shutdown() + + +def run_all_variations(i, lock, task_index, variation_count, results, file_lock, tasks): + """Each thread will choose one task and variation, and then gather + all the episodes_per_task for that variation.""" + + # Initialise each thread with random seed + np.random.seed(None) + num_tasks = len(tasks) + + img_size = list(map(int, FLAGS.image_size)) + + obs_config = ObservationConfig() + obs_config.set_all(True) + obs_config.right_shoulder_camera.image_size = img_size + obs_config.left_shoulder_camera.image_size = img_size + obs_config.overhead_camera.image_size = img_size + obs_config.wrist_camera.image_size = img_size + obs_config.front_camera.image_size = img_size + + # Store depth as 0 - 1 + obs_config.right_shoulder_camera.depth_in_meters = False + obs_config.left_shoulder_camera.depth_in_meters = False + obs_config.overhead_camera.depth_in_meters = False + obs_config.wrist_camera.depth_in_meters = False + obs_config.front_camera.depth_in_meters = False + + # We want to save the masks as rgb encodings. + obs_config.left_shoulder_camera.masks_as_one_channel = False + obs_config.right_shoulder_camera.masks_as_one_channel = False + obs_config.overhead_camera.masks_as_one_channel = False + obs_config.wrist_camera.masks_as_one_channel = False + obs_config.front_camera.masks_as_one_channel = False + + if FLAGS.renderer == 'opengl': + obs_config.right_shoulder_camera.render_mode = RenderMode.OPENGL + obs_config.left_shoulder_camera.render_mode = RenderMode.OPENGL + obs_config.overhead_camera.render_mode = RenderMode.OPENGL + obs_config.wrist_camera.render_mode = RenderMode.OPENGL + obs_config.front_camera.render_mode = RenderMode.OPENGL + + rlbench_env = Environment( + action_mode=MoveArmThenGripper(JointVelocity(), Discrete()), + obs_config=obs_config, + headless=True) + rlbench_env.launch() + + task_env = None + + tasks_with_problems = results[i] = '' + + while True: + # with lock: + if task_index.value >= num_tasks: + print('Process', i, 'finished') + break + + t = tasks[task_index.value] + + task_env = rlbench_env.get_task(t) + possible_variations = task_env.variation_count() + + variation_path = os.path.join( + FLAGS.save_path, task_env.get_name(), + VARIATIONS_ALL_FOLDER) + check_and_make(variation_path) + + episodes_path = os.path.join(variation_path, EPISODES_FOLDER) + check_and_make(episodes_path) + + abort_variation = False + for ex_idx in range(FLAGS.episodes_per_task): + attempts = 10 + while attempts > 0: + try: + variation = np.random.randint(possible_variations) + task_env = rlbench_env.get_task(t) + task_env.set_variation(variation) + descriptions, obs = task_env.reset() + + print('Process', i, '// Task:', task_env.get_name(), + '// Variation:', variation, '// Demo:', ex_idx) + + # TODO: for now we do the explicit looping. + demo, = task_env.get_demos( + amount=1, + live_demos=True) + except Exception as e: + attempts -= 1 + if attempts > 0: + continue + problem = ( + 'Process %d failed collecting task %s (variation: %d, ' + 'example: %d). Skipping this task/variation.\n%s\n' % ( + i, task_env.get_name(), variation, ex_idx, + str(e)) + ) + print(problem) + tasks_with_problems += problem + abort_variation = True + break + episode_path = os.path.join(episodes_path, EPISODE_FOLDER % ex_idx) + with file_lock: + save_demo(demo, episode_path, variation) + + with open(os.path.join( + episode_path, VARIATION_DESCRIPTIONS), 'wb') as f: + pickle.dump(descriptions, f) + break + if abort_variation: + break + + # with lock: + task_index.value += 1 + + results[i] = tasks_with_problems + rlbench_env.shutdown() + + +def main(argv): + + task_files = [t.replace('.py', '') for t in os.listdir(task.TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + + if len(FLAGS.tasks) > 0: + for t in FLAGS.tasks: + if t not in task_files: + raise ValueError('Task %s not recognised!.' % t) + task_files = FLAGS.tasks + + tasks = [task_file_to_task_class(t) for t in task_files] + + manager = Manager() + + result_dict = manager.dict() + file_lock = manager.Lock() + + task_index = manager.Value('i', 0) + variation_count = manager.Value('i', 0) + lock = manager.Lock() + + check_and_make(FLAGS.save_path) + + if FLAGS.all_variations: + # multiprocessing for all_variations not support (for now) + run_all_variations(0, lock, task_index, variation_count, result_dict, file_lock, tasks) + else: + processes = [Process( + target=run, args=( + i, lock, task_index, variation_count, result_dict, file_lock, + tasks)) + for i in range(FLAGS.processes)] + [t.start() for t in processes] + [t.join() for t in processes] + + print('Data collection done!') + for i in range(FLAGS.processes): + print(result_dict[i]) + + +if __name__ == '__main__': + app.run(main) diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/dataset_generator_bimanual.py b/third_party/AnyBimanual/third_party/RLBench/tools/dataset_generator_bimanual.py new file mode 100644 index 0000000000000000000000000000000000000000..4ad6ad73573e4ff4c3dabf73d950f3249224b050 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/dataset_generator_bimanual.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python3 + +import sys +import os +import logging +from functools import partial +import multiprocessing as mp +import pickle +import numpy as np + +from rlbench import ObservationConfig +from rlbench.observation_config import CameraConfig + +from rlbench.action_modes.action_mode import BimanualMoveArmThenGripper +from rlbench.action_modes.arm_action_modes import BimanualJointVelocity +from rlbench.action_modes.arm_action_modes import BimanualJointPosition +from rlbench.action_modes.gripper_action_modes import BimanualDiscrete + +from rlbench.backend.exceptions import BoundaryError, InvalidActionError, TaskEnvironmentError, WaypointError +from rlbench.backend.utils import task_file_to_task_class +from rlbench.environment import Environment +import rlbench.backend.task as task + +from PIL import Image +from rlbench.backend import utils +from rlbench.backend.const import * +from rlbench.backend.task import BIMANUAL_TASKS_PATH + + +import rich_click as click +from rich.logging import RichHandler +from click_prompt import choice_option +from click_prompt import filepath_option + + +camera_names = ["over_shoulder_left", "over_shoulder_right", "overhead", "wrist_right", "wrist_left", "front"] + + +def save_demo(demo, example_path, variation): + data_types = ["rgb", "depth", "point_cloud", "mask"] + #full_camera_names = list(map(lambda x: ('_'.join(x), x[-1]), product(camera_names, data_types))) + + # Save image data first, and then None the image data, and pickle + for i, obs in enumerate(demo): + for camera_name in camera_names: + for dtype in data_types: + + camera_full_name = f"{camera_name}_{dtype}" + data_path = os.path.join(example_path, camera_full_name) + # ..todo:: actually I prefer to abort if this one exists + os.makedirs(data_path, exist_ok=True) + + data = obs.perception_data.get(camera_full_name, None) + + if data is not None: + if dtype == 'rgb': + data = Image.fromarray(data) + elif dtype == 'depth': + data = utils.float_array_to_rgb_image(data, scale_factor=DEPTH_SCALE) + elif dtype == 'point_cloud': + continue + elif dtype == 'mask': + data = Image.fromarray((data * 255).astype(np.uint8)) + else: + raise Exception('Invalid data type') + logging.debug("saving %s", camera_full_name) + data.save(os.path.join(data_path, f"{dtype}_{i:04d}.png")) + + # ..why don't we put everything into a pickle file? + obs.perception_data.clear() + + # Save the low-dimension data + with open(os.path.join(example_path, LOW_DIM_PICKLE), 'wb') as f: + pickle.dump(demo, f) + + with open(os.path.join(example_path, VARIATION_NUMBER), 'wb') as f: + pickle.dump(variation, f) + + +def run_all_variations(task_name, headless, save_path, episodes_per_task, image_size): + """Each thread will choose one task and variation, and then gather + all the episodes_per_task for that variation.""" + + for handler in logging.root.handlers[:]: + logging.root.removeHandler(handler) + + from rich.logging import RichHandler + logging.basicConfig(level=logging.INFO, handlers=[RichHandler()]) + logging.root.name = task_name + + logging.info("Collecting data for %s", task_name) + + tasks = [task_file_to_task_class(task_name, True)] + + obs_config = ObservationConfig() + obs_config.set_all(True) + + default_config_params = {"image_size": image_size, "depth_in_meters": False, "masks_as_one_channel": False} + camera_configs = {camera_name: CameraConfig(**default_config_params) for camera_name in camera_names} + obs_config.camera_configs = camera_configs + + + # ..record date with BimanualJointPosition + robot_setup = 'dual_panda' + rlbench_env = Environment( + action_mode=BimanualMoveArmThenGripper(BimanualJointPosition(), BimanualDiscrete()), + obs_config=obs_config, + robot_setup=robot_setup, + headless=headless) + + rlbench_env.launch() + + tasks_with_problems = "" + + for task in tasks: + + task_env = rlbench_env.get_task(task) + possible_variations = task_env.variation_count() + + logging.info("Task has %s possible variations", possible_variations) + + variation_path = os.path.join(save_path, task_env.get_name(), VARIATIONS_ALL_FOLDER) + os.makedirs(variation_path, exist_ok=True) + + episodes_path = os.path.join(variation_path, EPISODES_FOLDER) + os.makedirs(episodes_path, exist_ok=True) + + + abort_variation = False + for ex_idx in range(episodes_per_task): + attempts = 10 + while attempts > 0: + try: + variation = np.random.randint(possible_variations) + + task_env = rlbench_env.get_task(task) + + task_env.set_variation(variation) + descriptions, obs = task_env.reset() + + logging.info("// Task: %s Variation %s Demo %s", task_env.get_name(), variation, ex_idx) + + # TODO: for now we do the explicit looping. + demo, = task_env.get_demos(amount=1, live_demos=True) + # NoWaypointsError, DemoError, + except (BoundaryError, WaypointError, InvalidActionError, TaskEnvironmentError) as e: + logging.warning("Exception %s", e) + attempts -= 1 + if attempts > 0: + continue + problem = ( + 'Failed collecting task %s (variation: %d, ' + 'example: %d). Skipping this task/variation.\n%s\n' % ( + task_env.get_name(), variation, ex_idx,str(e)) + ) + logging.error(problem) + tasks_with_problems += problem + abort_variation = True + break + + episode_path = os.path.join(episodes_path, EPISODE_FOLDER % ex_idx) + + save_demo(demo, episode_path, variation) + + with open(os.path.join( episode_path, VARIATION_DESCRIPTIONS), 'wb') as f: + pickle.dump(descriptions, f) + + break + if abort_variation: + break + + + rlbench_env.shutdown() + + return tasks_with_problems + + + +def get_bimanual_tasks(): + tasks = [t.replace('.py', '') for t in + os.listdir(BIMANUAL_TASKS_PATH) if t != '__init__.py' and t.endswith('.py')] + return sorted(tasks) + + +@click.command() +@filepath_option("--save_path", default="/tmp/rlbench_data/", help="Where to save the demos.") +@choice_option('--tasks', type=click.Choice(get_bimanual_tasks()), multiple=True, help='The tasks to collect. If empty, all tasks are collected.') +@click.option("--episodes_per_task", default=10, help="The number of episodes to collect per task.", prompt="Number of episodes") +@click.option("--all_variations", is_flag=True, default=True, help="Include all variations when sampling epsiodes") +#@click.option("--variations", default=-1, help="Number of variations to collect per task. -1 for all.") +@click.option("--headless/--no-headless", default=True, is_flag=True, help='Hide the simulator window') +#@click.option("--color-robot/--no-color-robot", default=False, is_flag=True, help='Colorize') +@choice_option('--image-size', type=click.Choice(["128x128", "256x256", "640x480"]), multiple=False, help='Select the image_size (width, height)') +def main(save_path, tasks, episodes_per_task, all_variations, headless, image_size): + + # ..todo check if already exits + + mp.set_start_method("spawn") + + logging.basicConfig(level=logging.INFO, handlers=[RichHandler()]) + + np.random.seed(None) + + ctx = mp.get_context('spawn') + + if not tasks: + logging.error("No tasks selected!") + + + logging.info("Generating %s episodes for each tasks %s with image size %s", episodes_per_task, tasks, image_size) + + + image_size = list(map(int, image_size.split("x"))) + + os.makedirs(save_path, exist_ok=True) + + if not all_variations: + logging.error("Variations not supported") + sys.exit(-1) + + logging.debug("Selected tasks %s", tasks) + + fn = partial(run_all_variations, headless=headless, save_path=save_path, episodes_per_task=episodes_per_task, image_size=image_size) + with ctx.Pool(processes=4) as pool: + pool.map(fn, tasks) + + +if __name__ == '__main__': + main() + + diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/task_builder.py b/third_party/AnyBimanual/third_party/RLBench/tools/task_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..7cbdcb8bec2b154a990968717d701deb1314ff34 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/task_builder.py @@ -0,0 +1,359 @@ +import os +import sys +from os.path import join, dirname, abspath, isfile + +CURRENT_DIR = dirname(abspath(__file__)) +sys.path.insert(0, join(CURRENT_DIR, '..')) # Use local RLBench rather than installed + +import traceback +import readline + +from pyrep.const import RenderMode + +from rlbench.backend import task +from rlbench.backend.const import TTT_FILE +from pyrep import PyRep +from pyrep.robots.arms.panda import Panda +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from rlbench.backend.scene import Scene +from rlbench.backend.exceptions import * +from rlbench.observation_config import ObservationConfig, CameraConfig +from rlbench.backend.robot import Robot +from rlbench.backend.robot import UnimanualRobot +from rlbench.utils import name_to_task_class +from tools.task_validator import task_smoke, TaskValidationError +import shutil + +CURRENT_DIR = dirname(abspath(__file__)) + + +def print_fail(message, end='\n'): + message = str(message) + sys.stderr.write('\x1b[1;31m' + message.strip() + '\x1b[0m' + end) + + +def setup_list_completer(): + task_files = [t.replace('.py', '') for t in os.listdir(task.TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + + def list_completer(_, state): + line = readline.get_line_buffer() + if not line: + return [c + " " for c in task_files][state] + + else: + return [c + " " for c in task_files if c.startswith(line)][state] + + readline.parse_and_bind("tab: complete") + readline.set_completer(list_completer) + + +class LoadedTask(object): + + def __init__(self, pr: PyRep, scene: Scene, robot: Robot): + self.pr = pr + self.scene = scene + self.robot = robot + self.task = self.task_class = self.task_file = None + self._variation_index = 0 + + def _load_task_to_scene(self): + self.scene.unload() + self.task = self.task_class( + self.pr, self.robot, self.task_file.replace('.py', '')) + try: + # Try and load the task + scene.load(self.task) + except FileNotFoundError as e: + # The .ttt file must not exist + handle = Dummy.create() + handle.set_name(self.task_file.replace('.py', '')) + handle.set_model(True) + # Put the dummy at the centre of the workspace + self.task.get_base().set_position(Shape('workspace').get_position()) + + def _edit_new_task(self): + task_file = input('What task would you like to edit?\n') + task_file = task_file.strip(' ') + if len(task_file) > 3 and task_file[-3:] != '.py': + task_file += '.py' + try: + task_class = name_to_task_class(task_file) + except: + print('There was no task named: %s. ' + 'Would you like to create it?' % task_file) + inp = input() + if inp == 'y': + self._create_python_file(task_file) + task_class = name_to_task_class(task_file) + else: + print('Please pick a defined task in that case.') + task_class, task_file = self._edit_new_task() + return task_class, task_file + + def _create_python_file(self, task_file: str): + with open(join(CURRENT_DIR, 'assets', 'task_template.txt'), 'r') as f: + file_content = f.read() + class_name = self._file_to_class_name(task_file) + file_content = file_content % (class_name,) + new_file_path = join(CURRENT_DIR, '../rlbench/tasks', task_file) + if isfile(new_file_path): + raise RuntimeError('File already exists. Will not override this.') + with open(new_file_path, 'w+') as f: + f.write(file_content) + + def _file_to_class_name(self, name): + name = name.replace('.py', '') + return ''.join([w[0].upper() + w[1:] for w in name.split('_')]) + + def reload_python(self): + try: + task_class = name_to_task_class(self.task_file) + except Exception as e: + print_fail('The python file could not be loaded!') + traceback.print_exc() + return None, None + self.task = task_class( + self.pr, self.robot, self.task_file.replace('.py', '')) + self.scene.load(self.task) + + def new_task(self): + self._variation_index = 0 + self.task_class, self.task_file = self._edit_new_task() + self._load_task_to_scene() + self.pr.step_ui() + print('You are now editing: %s' % str(self.task_class)) + + def reset_variation(self): + self._variation_index = 0 + + def new_variation(self): + try: + self._variation_index += 1 + descriptions = self.scene.init_episode( + self._variation_index % self.task.variation_count(), + max_attempts=10) + print('Task descriptions: ', descriptions) + except (WaypointError, BoundaryError, Exception) as ex: + traceback.print_exc() + self.pr.step_ui() + + def new_episode(self): + try: + descriptions = self.scene.init_episode( + self._variation_index % self.task.variation_count(), + max_attempts=10) + print('Task descriptions: ', descriptions) + except (WaypointError, BoundaryError, Exception) as ex: + traceback.print_exc() + self.scene.reset() + self.pr.step_ui() + + def new_demo(self): + try: + self.scene.get_demo(False, randomly_place=False) + except (WaypointError, NoWaypointsError, DemoError, Exception) as e: + traceback.print_exc() + success, terminate = self.task.success() + if success: + print("Demo was a success!") + self.scene.reset() + self.pr.step_ui() + self.pr.step_ui() + + def save_task(self): + ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + self.task_file.replace('.py', '.ttm')) + self.task.get_base().save_model(ttm_path) + print('Task saved to:', ttm_path) + + def run_task_validator(self): + print('About to perform task validation.') + print("What variation to test? Pick int in range: 0 to %d, or -1 to " + "test all. Or press 'e' to exit." + % self.task.variation_count()) + inp = input() + if inp == 'e': + return + self.pr.start() + try: + v = int(inp) + v = v if v < 0 else v % self.task.variation_count() + task_smoke(self.task, self.scene, variation=v) + except TaskValidationError as e: + traceback.print_exc() + self.pr.stop() + + def rename(self): + print('Enter new name (or q to abort).') + inp = input() + if inp == 'q': + return + + name = inp.replace('.py', '') + python_file = name + '.py' + + # Change name of base + handle = Dummy(self.task_file.replace('.py', '')) + handle.set_name(name) + + # Change the class name + old_file_path = join(CURRENT_DIR, '../rlbench/tasks', self.task_file) + old_class_name = self._file_to_class_name(self.task_file) + new_class_name = self._file_to_class_name(name) + with open(old_file_path, 'r') as f: + content = f.read() + content = content.replace(old_class_name, new_class_name) + with open(old_file_path, 'w') as f: + f.write(content) + + # Rename python task file + new_file_path = join(CURRENT_DIR, '../rlbench/tasks', python_file) + os.rename(old_file_path, new_file_path) + + # Rename .ttt + old_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + self.task_file.replace('.py', '.ttm')) + new_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + python_file.replace('.py', '.ttm')) + os.rename(old_ttm_path, new_ttm_path) + + self.task_file = python_file + self.reload_python() + self.save_task() + print('Rename complete!') + + def duplicate_task(self): + print('Enter new name for duplicate (or q to abort).') + inp = input() + if inp == 'q': + return + + name = inp.replace('.py', '') + new_python_file = name + '.py' + + # Change the class name + old_file_path = join(CURRENT_DIR, '../rlbench/tasks', self.task_file) + old_class_name = self._file_to_class_name(self.task_file) + new_file_path = join(CURRENT_DIR, '../rlbench/tasks', new_python_file) + new_class_name = self._file_to_class_name(name) + + if os.path.isfile(new_file_path): + print('File: %s already exists!' % new_file_path) + return + + # Change name of base + handle = Dummy(self.task_file.replace('.py', '')) + handle.set_name(name) + + with open(old_file_path, 'r') as f: + content = f.read() + content = content.replace(old_class_name, new_class_name) + with open(new_file_path, 'w') as f: + f.write(content) + + # Rename .ttt + old_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + self.task_file.replace('.py', '.ttm')) + new_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + new_python_file.replace('.py', '.ttm')) + shutil.copy(old_ttm_path, new_ttm_path) + + self.task_file = new_python_file + self.reload_python() + self.save_task() + print('Duplicate complete!') + + +if __name__ == '__main__': + + setup_list_completer() + + pr = PyRep() + ttt_file = join(CURRENT_DIR, '..', 'rlbench', TTT_FILE) + pr.launch(ttt_file, responsive_ui=True) + pr.step_ui() + + robot = UnimanualRobot(Panda(), PandaGripper()) + cam_config = CameraConfig(rgb=True, depth=False, mask=False, + render_mode=RenderMode.OPENGL) + obs_config = ObservationConfig() + obs_config.set_all(False) + obs_config.right_shoulder_camera = cam_config + obs_config.left_shoulder_camera = cam_config + obs_config.overhead_camera = cam_config + obs_config.wrist_camera = cam_config + obs_config.front_camera = cam_config + + scene = Scene(pr, robot, obs_config) + loaded_task = LoadedTask(pr, scene, robot) + + print(' ,') + print(' /( ___________') + print('| >:===========` Welcome to task builder!') + print(' )(') + print(' ""') + + loaded_task.new_task() + + while True: + os.system('cls' if os.name == 'nt' else 'clear') + print('\n-----------------\n') + print('The python file will be reloaded when simulation is restarted.') + print('(q) to quit.') + if pr.running: + print('(+) stop the simulator') + print('(v) for task variation.') + print('(e) for episode of same variation.') + print('(d) for demo.') + print('(p) for running the sim for 100 steps (with rendering).') + else: + print('(!) to run task validator.') + print('(+) run the simulator') + print('(n) for new task.') + print('(s) to save the .ttm') + print('(r) to rename the task') + print('(u) to duplicate/copy the task') + + inp = input() + + if inp == 'q': + break + + if pr.running: + if inp == '+': + pr.stop() + pr.step_ui() + elif inp == 'p': + [(pr.step(), scene.get_observation()) for _ in range(100)] + elif inp == 'd': + loaded_task.new_demo() + elif inp == 'v': + loaded_task.new_variation() + elif inp == 'e': + loaded_task.new_episode() + else: + if inp == '+': + loaded_task.reload_python() + loaded_task.reset_variation() + pr.start() + pr.step_ui() + elif inp == 'n': + inp = input('Do you want to save the current task first?\n') + if inp == 'y': + loaded_task.save_task() + loaded_task.new_task() + elif inp == 's': + loaded_task.save_task() + elif inp == '!': + loaded_task.run_task_validator() + elif inp == 'r': + loaded_task.rename() + elif inp == 'u': + loaded_task.duplicate_task() + + pr.stop() + pr.shutdown() + print('Done. Goodbye!') diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/task_builder_bimanual.py b/third_party/AnyBimanual/third_party/RLBench/tools/task_builder_bimanual.py new file mode 100644 index 0000000000000000000000000000000000000000..f84a3a6c00d86d847d4644bd124a216c9bc49055 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/task_builder_bimanual.py @@ -0,0 +1,443 @@ +#!/usr/bin/env python +import os +import sys +from os.path import join, dirname, abspath, isfile + +import logging + +from pyrep.robots.arms.dual_panda import PandaLeft, PandaRight +from pyrep.robots.end_effectors.dual_panda_gripper import PandaGripperLeft, PandaGripperRight + +CURRENT_DIR = dirname(abspath(__file__)) +sys.path.insert(0, join(CURRENT_DIR, '..')) # Use local RLBench rather than installed + +import traceback +import readline + +import rich_click as click +from click_prompt import auto_complete_argument + +from pyrep.const import RenderMode + +from rlbench.backend import task +from rlbench.backend.const import BIMANUAL_TTT_FILE +from pyrep import PyRep +from pyrep.robots.arms.panda import Panda +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from rlbench.backend.scene import Scene +from rlbench.backend.exceptions import * +from rlbench.observation_config import ObservationConfig, CameraConfig +from rlbench.backend.robot import Robot +from rlbench.backend.robot import BimanualRobot +from rlbench.backend.robot import UnimanualRobot +from rlbench.utils import name_to_task_class +from tools.task_validator import task_smoke, TaskValidationError +import shutil + + +from yarr.utils.video_utils import CircleCameraMotion +from yarr.utils.video_utils import TaskRecorder + +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +from rich.logging import RichHandler + + +CURRENT_DIR = dirname(abspath(__file__)) + + +def print_fail(message, end='\n'): + message = str(message) + sys.stderr.write('\x1b[1;31m' + message.strip() + '\x1b[0m' + end) + + +def setup_list_completer(): + task_files = [t.replace('.py', '') for t in os.listdir(task.BIMANUAL_TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + + def list_completer(_, state): + line = readline.get_line_buffer() + if not line: + return [c + " " for c in task_files][state] + + else: + return [c + " " for c in task_files if c.startswith(line)][state] + + readline.parse_and_bind("tab: complete") + readline.set_completer(list_completer) + + +class LoadedTask(object): + + def __init__(self, pr: PyRep, scene: Scene, robot: Robot): + self.pr = pr + self.scene = scene + self.robot = robot + self.task = self.task_class = self.task_file = None + self._variation_index = 0 + + def _load_task_to_scene(self): + self.scene.unload() + self.task = self.task_class( + self.pr, self.robot, self.task_file.replace('.py', '')) + try: + # Try and load the task + scene.load(self.task) + except FileNotFoundError as e: + # The .ttt file must not exist + handle = Dummy.create() + handle.set_name(self.task_file.replace('.py', '')) + handle.set_model(True) + # Put the dummy at the centre of the workspace + self.task.get_base().set_position(Shape('workspace').get_position()) + + def _edit_new_task(self): + task_file = input('What task would you like to edit?\n') + task_file = task_file.strip(' ') + if len(task_file) > 3 and task_file[-3:] != '.py': + task_file += '.py' + try: + task_class = name_to_task_class(task_file, True) + except: + print('There was no task named: %s. ' + 'Would you like to create it?' % task_file) + inp = input() + if inp == 'y': + self._create_python_file(task_file) + task_class = name_to_task_class(task_file, True) + else: + print('Please pick a defined task in that case.') + task_class, task_file = self._edit_new_task() + return task_class, task_file + + def _create_python_file(self, task_file: str): + with open(join(CURRENT_DIR, 'assets', 'bimanual_task_template.txt'), 'r') as f: + file_content = f.read() + class_name = self._file_to_class_name(task_file) + file_content = file_content % (class_name,) + new_file_path = join(CURRENT_DIR, '../rlbench/bimanual_tasks', task_file) + if isfile(new_file_path): + raise RuntimeError('File already exists. Will not override this.') + with open(new_file_path, 'w+') as f: + f.write(file_content) + + def _file_to_class_name(self, name): + name = name.replace('.py', '') + return ''.join([w[0].upper() + w[1:] for w in name.split('_')]) + + def reload_python(self): + try: + task_class = name_to_task_class(self.task_file, True) + except Exception as e: + print_fail('The python file could not be loaded!') + traceback.print_exc() + return None, None + self.task = task_class( + self.pr, self.robot, self.task_file.replace('.py', '')) + self.scene.load(self.task) + + def new_task(self): + self._variation_index = 0 + self.task_class, self.task_file = self._edit_new_task() + self._load_task_to_scene() + self.pr.step_ui() + print('You are now editing: %s' % str(self.task_class)) + + def reset_variation(self): + self._variation_index = 0 + + def new_variation(self): + try: + self._variation_index += 1 + descriptions = self.scene.init_episode( + self._variation_index % self.task.variation_count(), + max_attempts=10) + print('Task descriptions: ', descriptions) + except (WaypointError, BoundaryError, Exception) as ex: + traceback.print_exc() + self.pr.step_ui() + + def new_episode(self): + try: + descriptions = self.scene.init_episode( + self._variation_index % self.task.variation_count(), + max_attempts=10) + print('Task descriptions: ', descriptions) + except (WaypointError, BoundaryError, Exception) as ex: + traceback.print_exc() + self.scene.reset() + self.pr.step_ui() + + + def record_demo(self): + cam_placeholder = Dummy('cam_cinematic_placeholder') + cam = VisionSensor.create([1920, 1200], background_color=[1.0, 1.0, 1.0]) + cam.set_pose(cam_placeholder.get_pose()) + cam.set_parent(cam_placeholder) + + + scene_background = ["Wall1", "Wall2", "Wall3", "Wall4", "Floor", "Roof", "ResizableFloor_5_25_visibleElement"] + + for object_name in scene_background: + Shape(object_name).set_renderable(False) + + cam_motion = CircleCameraMotion(cam, Dummy('cam_cinematic_base'), 0.005) + tr = TaskRecorder(None, cam_motion, fps=30) + + try: + self.scene.get_demo(False, callable_each_step=tr.take_snap, randomly_place=False) + except (WaypointError, NoWaypointsError, DemoError, Exception) as e: + traceback.print_exc() + success, terminate = self.task.success() + + + recording_output_path = f"/tmp/rlbench_video_{self.task_file[:-3]}.mp4" + + tr.save(recording_output_path, f"Demo for task {self.task_file} success={success}", 1 ) + + print(f"Saving video to {recording_output_path}") + + for object_name in scene_background: + Shape(object_name).set_renderable(True) + + if success: + print("Demo was a success!") + self.scene.reset() + self.pr.step_ui() + + + + + def new_demo(self): + try: + self.scene.get_demo(False, randomly_place=False) + except (WaypointError, NoWaypointsError, DemoError, Exception) as e: + traceback.print_exc() + success, terminate = self.task.success() + if success: + print("Demo was a success!") + self.scene.reset() + self.pr.step_ui() + self.pr.step_ui() + + def save_task(self): + ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + self.task_file.replace('.py', '.ttm')) + self.task.get_base().save_model(ttm_path) + print('Task saved to:', ttm_path) + + def run_task_validator(self): + print('About to perform task validation.') + print("What variation to test? Pick int in range: 0 to %d, or -1 to " + "test all. Or press 'e' to exit." + % self.task.variation_count()) + inp = input() + if inp == 'e': + return + self.pr.start() + try: + v = int(inp) + v = v if v < 0 else v % self.task.variation_count() + task_smoke(self.task, self.scene, variation=v) + except TaskValidationError as e: + traceback.print_exc() + self.pr.stop() + + def rename(self): + print('Enter new name (or q to abort).') + inp = input() + if inp == 'q': + return + + name = inp.replace('.py', '') + python_file = name + '.py' + + # Change name of base + handle = Dummy(self.task_file.replace('.py', '')) + handle.set_name(name) + + # Change the class name + old_file_path = join(CURRENT_DIR, '../rlbench/bimanual_tasks', self.task_file) + old_class_name = self._file_to_class_name(self.task_file) + new_class_name = self._file_to_class_name(name) + with open(old_file_path, 'r') as f: + content = f.read() + content = content.replace(old_class_name, new_class_name) + with open(old_file_path, 'w') as f: + f.write(content) + + # Rename python task file + new_file_path = join(CURRENT_DIR, '../rlbench/bimanual_tasks', python_file) + os.rename(old_file_path, new_file_path) + + # Rename .ttt + old_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + self.task_file.replace('.py', '.ttm')) + new_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + python_file.replace('.py', '.ttm')) + os.rename(old_ttm_path, new_ttm_path) + + self.task_file = python_file + self.reload_python() + self.save_task() + print('Rename complete!') + + def duplicate_task(self): + print('Enter new name for duplicate (or q to abort).') + inp = input() + if inp == 'q': + return + + name = inp.replace('.py', '') + new_python_file = name + '.py' + + # Change the class name + old_file_path = join(CURRENT_DIR, '../rlbench/bimanual_tasks', self.task_file) + old_class_name = self._file_to_class_name(self.task_file) + new_file_path = join(CURRENT_DIR, '../rlbench/bimanual_tasks', new_python_file) + new_class_name = self._file_to_class_name(name) + + if os.path.isfile(new_file_path): + print('File: %s already exists!' % new_file_path) + return + + # Change name of base + handle = Dummy(self.task_file.replace('.py', '')) + handle.set_name(name) + + with open(old_file_path, 'r') as f: + content = f.read() + content = content.replace(old_class_name, new_class_name) + with open(new_file_path, 'w') as f: + f.write(content) + + # Rename .ttt + old_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + self.task_file.replace('.py', '.ttm')) + new_ttm_path = join(CURRENT_DIR, '../rlbench/task_ttms', + new_python_file.replace('.py', '.ttm')) + shutil.copy(old_ttm_path, new_ttm_path) + + self.task_file = new_python_file + self.reload_python() + self.save_task() + print('Duplicate complete!') + + + +NEW_TASKS = 'New' + +def get_all_available_tasks(): + return [t.replace('.py', '') for t in os.listdir(task.BIMANUAL_TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + + + + + +if __name__ == '__main__': + + + + logging.basicConfig(level=logging.DEBUG, handlers=[RichHandler()]) + + + setup_list_completer() + + pr = PyRep() + ttt_file = join(CURRENT_DIR, '..', 'rlbench', BIMANUAL_TTT_FILE) + pr.launch(ttt_file, responsive_ui=True) + pr.step_ui() + + + robot = BimanualRobot(PandaRight(), PandaGripperRight(), PandaLeft(), PandaGripperLeft()) + + + cam_config = CameraConfig(rgb=True, depth=False, mask=False, + render_mode=RenderMode.OPENGL) + obs_config = ObservationConfig() + obs_config.set_all(False) + obs_config.right_shoulder_camera = cam_config + obs_config.left_shoulder_camera = cam_config + obs_config.overhead_camera = cam_config + obs_config.wrist_camera = cam_config + obs_config.front_camera = cam_config + + scene = Scene(pr, robot, obs_config) + loaded_task = LoadedTask(pr, scene, robot) + + print(' ,') + print(' /( ___________') + print('| >:===========` Welcome to task builder!') + print(' )(') + print(' ""') + + loaded_task.new_task() + + while True: + os.system('cls' if os.name == 'nt' else 'clear') + print('\n-----------------\n') + print('The python file will be reloaded when simulation is restarted.') + print('(q) to quit.') + if pr.running: + print('(+) stop the simulator') + print('(v) for task variation.') + print('(e) for episode of same variation.') + print('(d) for demo.') + print('(r) record a video of the demo.') + print('(p) for running the sim for 100 steps (with rendering).') + else: + print('(!) to run task validator.') + print('(+) run the simulator') + print('(n) for new task.') + print('(s) to save the .ttm') + print('(r) to rename the task') + print('(u) to duplicate/copy the task') + + inp = input() + + if inp == 'q': + break + + if pr.running: + if inp == '+': + pr.stop() + pr.step_ui() + elif inp == 'p': + [(pr.step(), scene.get_observation()) for _ in range(100)] + elif inp == 'd': + loaded_task.new_demo() + input('Press enter to continue') + elif inp == 'r': + loaded_task.record_demo() + input('Press enter to continue') + elif inp == 'v': + loaded_task.new_variation() + elif inp == 'e': + loaded_task.new_episode() + else: + if inp == '+': + loaded_task.reload_python() + loaded_task.reset_variation() + pr.start() + pr.step_ui() + elif inp == 'n': + inp = input('Do you want to save the current task first?\n') + if inp == 'y': + loaded_task.save_task() + loaded_task.new_task() + elif inp == 's': + loaded_task.save_task() + elif inp == '!': + loaded_task.run_task_validator() + elif inp == 'r': + loaded_task.rename() + elif inp == 'u': + loaded_task.duplicate_task() + + pr.stop() + pr.shutdown() + print('Done. Goodbye!') diff --git a/third_party/AnyBimanual/third_party/RLBench/tools/task_validator.py b/third_party/AnyBimanual/third_party/RLBench/tools/task_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..6d7bbf50ff4467113da1cb75346a6e9f65f4105a --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tools/task_validator.py @@ -0,0 +1,150 @@ +from rlbench.backend.task import Task +from rlbench.backend.scene import DemoError +from rlbench.observation_config import ObservationConfig +from pyrep import PyRep +from pyrep.robots.arms.panda import Panda +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from rlbench.backend.const import TTT_FILE +from rlbench.backend.scene import Scene +from rlbench.backend.utils import task_file_to_task_class +from rlbench.backend.task import TASKS_PATH +from rlbench.backend.robot import Robot +from rlbench.backend.robot import UnimanualRobot +import numpy as np +import os +import argparse + +DEMO_ATTEMPTS = 5 +MAX_VARIATIONS = 100 + + +class TaskValidationError(Exception): + pass + + +def task_smoke(task: Task, scene: Scene, variation=-1, demos=4, success=0.50, + max_variations=3, test_demos=True): + # -1 variations for all. + + print('Running task validator on task: %s' % task.get_name()) + + # Loading + scene.load(task) + + # Number of variations + variation_count = task.variation_count() + if variation_count < 0: + raise TaskValidationError( + "The method 'variation_count' should return a number > 0.") + + if variation_count > MAX_VARIATIONS: + raise TaskValidationError( + "This task had %d variations. Currently the limit is set to %d" % + (variation_count, MAX_VARIATIONS)) + + # Base rotation bounds + base_pos, base_ori = task.base_rotation_bounds() + if len(base_pos) != 3 or len(base_ori) != 3: + raise TaskValidationError( + "The method 'base_rotation_bounds' should return a tuple " + "containing a list of floats.") + + # Boundary root + root = task.boundary_root() + if not root.still_exists(): + raise TaskValidationError( + "The method 'boundary_root' should return a Dummy that is the root " + "of the task.") + + def variation_smoke(i): + + print('Running task validator on variation: %d' % i) + + attempt_result = False + failed_demos = 0 + for j in range(DEMO_ATTEMPTS): + failed_demos = run_demos(i) + attempt_result = (failed_demos / float(demos) <= 1. - success) + if attempt_result: + break + else: + print('Failed on attempt %d. Trying again...' % j) + + # Make sure we don't fail too often + if not attempt_result: + raise TaskValidationError( + "Too many failed demo runs. %d of %d demos failed." % ( + failed_demos, demos)) + else: + print('Variation %d of task %s is good!' % (i, task.get_name())) + if test_demos: + print('%d of %d demos were successful.' % ( + demos - failed_demos, demos)) + + def run_demos(variation_num): + fails = 0 + for dr in range(demos): + try: + scene.reset() + desc = scene.init_episode(variation_num, max_attempts=10) + if not isinstance(desc, list) or len(desc) <= 0: + raise TaskValidationError( + "The method 'init_variation' should return a list of " + "string descriptions.") + if test_demos: + demo = scene.get_demo(record=True) + assert len(demo) > 0 + except DemoError as e: + fails += 1 + print(e) + except Exception as e: + # TODO: check that we don't fall through all of these cases + fails += 1 + print(e) + + return fails + + variations_to_test = [variation] + if variation < 0: + variations_to_test = list(range( + np.minimum(variation_count, max_variations))) + + # Task set-up + scene.init_task() + [variation_smoke(i) for i in variations_to_test] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("task", help="The task file to test.") + args = parser.parse_args() + + python_file = os.path.join(TASKS_PATH, args.task) + if not os.path.isfile(python_file): + raise RuntimeError('Could not find the task file: %s' % python_file) + + task_class = task_file_to_task_class(args.task) + + DIR_PATH = os.path.dirname(os.path.abspath(__file__)) + sim = PyRep() + ttt_file = os.path.join( + DIR_PATH, '..', 'rlbench', TTT_FILE) + sim.launch(ttt_file, headless=True) + sim.step_ui() + sim.set_simulation_timestep(0.005) + sim.step_ui() + sim.start() + + robot = UnimanualRobot(Panda(), PandaGripper()) + + active_task = task_class(sim, robot) + obs = ObservationConfig() + obs.set_all(False) + scene = Scene(sim, robot, obs) + try: + task_smoke(active_task, scene, variation=2) + except TaskValidationError as e: + sim.shutdown() + raise e + sim.shutdown() + print('Validation successful!') diff --git a/third_party/AnyBimanual/third_party/RLBench/travisci_generate_index.py b/third_party/AnyBimanual/third_party/RLBench/travisci_generate_index.py new file mode 100644 index 0000000000000000000000000000000000000000..008fb4e8feb6f885727b524905a8ee78d26d5499 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/travisci_generate_index.py @@ -0,0 +1,59 @@ +import sys +import os +import re +import shutil + +TEST_TYPES = ['unit', 'demos'] +HISTORY_TO_KEEP = 30 + + +if __name__ == '__main__': + + assert len(sys.argv) == 5 + + test_type = sys.argv[1] + branch = sys.argv[2] + build_num = sys.argv[3] + exit_code = sys.argv[4] + + fail = int(exit_code) != 0 + + assert test_type in TEST_TYPES + + uls = [] + if os.path.exists('index.html'): + with open('index.html', 'r') as f: + lines = f.read() + uls = [out[0] for out in re.findall("(
    ((.|\n)*?)
)", lines)] + if len(uls) > HISTORY_TO_KEEP - 1: + uls = uls[:HISTORY_TO_KEEP - 1] + + reports = sorted(os.listdir('reports')) + + if len(reports) > HISTORY_TO_KEEP: + for delete_folder in reports[:-HISTORY_TO_KEEP]: + shutil.rmtree(os.path.join('reports', delete_folder)) + + color = "style='color:Tomato;'" if fail else "style='color:MediumSeaGreen;'" + new_entry = ("\n" % + (color, build_num, test_type, build_num, branch, test_type, + 'Fail!' if fail else 'Pass!')) + + html_top = """ + + + +

RLBench Test Page

+ """ + html_bottom = """ + + + """ + + new_html = html_top + new_entry + '\n'.join(uls) + html_bottom + with open('index.html', 'w') as f: + f.write(new_html) + + # Will be 0 if tests ran OK + sys.exit(fail) diff --git a/third_party/AnyBimanual/third_party/RLBench/travisci_run_tests.py b/third_party/AnyBimanual/third_party/RLBench/travisci_run_tests.py new file mode 100644 index 0000000000000000000000000000000000000000..494a447c91ed94ed0c290c9e820d4910fa08cba2 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/travisci_run_tests.py @@ -0,0 +1,34 @@ +from unittest import TestSuite, defaultTestLoader +from HtmlTestRunner import HTMLTestRunner +import sys +import os + +TEST_TYPES = ['unit', 'demos'] + + +if __name__ == '__main__': + + test_folder = 'tests' + + assert len(sys.argv) == 4 + + test_type = sys.argv[1] + branch = sys.argv[2] + build_num = sys.argv[3] + + assert test_type in TEST_TYPES + + test_suite = TestSuite() + all_test_cases = defaultTestLoader.discover( + os.path.join(test_folder, test_type), 'test*.py') + # Loop the found test cases and add them into test suite. + for test_case in all_test_cases: + test_suite.addTests(test_case) + runner = HTMLTestRunner( + output='reports/%s' % str(build_num), + report_name=test_type, + add_timestamp=False, combine_reports=True) + result = runner.run(test_suite) + + # Will be 0 if tests ran OK + sys.exit(1 if len(result.failures) + len(result.errors) > 0 else 0) diff --git a/third_party/AnyBimanual/third_party/RLBench/tutorials/complex_task.md b/third_party/AnyBimanual/third_party/RLBench/tutorials/complex_task.md new file mode 100644 index 0000000000000000000000000000000000000000..857784d438b63dabf59636058823324937f4a988 --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tutorials/complex_task.md @@ -0,0 +1,310 @@ +# Complex Task Walkthrough + +Here we will walk through the entire process of building a more complex task, empty_container. Hopefully you will have something that looks like the following by the end: + + + +This tutorial will assume some of the knowledge learnt in the Simple Task Walkthrough, so make sure to work through that first. As for every task built, we will again go through each of the stages: (1) creating the task through the task builder, then (2) creating the task by adding objects to the scene in a V-Rep simulator instance, (3) coding the Python File, then finally (4) passing the Task Validator. + +In addition to the features common to all tasks, this task will need to excute the following: +* **Dynamically Sample and Spawn Procedural Objects** - These are a collection of various small shapes as models downloaded as .obj and .mtl files from https://sites.google.com/site/brainrobotdata/home/models. If you wish to understand how these object files fit into the repository, examine the rlbench/assets/procedural_objects directory which at minimum must contain the {*object_name*} and {*object_name*}*\_coll* files for every object. +* **Setting target container colors** - The target containers will cycle through the colors list found in *rlbench/backend/const.py* with every change in variation. +* **Grasping** - Many tasks require the Panda to grasp and pick objects up. We will see how waypoints can be given extension strings to control this functionality. +* **Dynamically Repeat Waypoints** - For every object to be removed from the large container, the same set of waypoints will be used, with the Panda's path being re-calculated for every remaining object to be moved. The only difference between these paths will be the position of the grasping dummy *waypoint1*, so we will need to define a function which re-positions this dummy to another object with every repetition. +* **Account for Failed Grasps** - Many of the procedural objects are awkwardly shaped and thus difficult to grasp. When the Panda fails to grasp an item at *waypoint1*, it will still complete its path (if reachable) with nothing grasped. In order to raise the percentage of succesful demos and thus pass the task validator, we would like to account for this. This will be done by assessing which objects have been succesfully moved at every time step in the *step()* function, and instructing the task to re-position and repeat waypoints until all items have been moved to the target container. +* **Cleanup Dynamic Objects** - The state of the scene before a demo must match the state of the scene upon demo completion. As such, we will use the *cleanup()* function to remove all of the procedural objects from the V-Rep scene upon completion of every demo. + +## A note on Grasping and Physics in V-Rep +It is worth discussing here how the Panda grasps objects in the simulator, and how this differs from how a human hand would grasp in the real world. Rather than rely on friction and reactionary force as a person would when picking something up, RLBench and PyRep simplify physics calculations (and thus speed up and smooth out simulation) by making any object upon which the Panda's gripper closes on a child of the Panda tip, as long as said object has been registered as graspable in the python file. This keeps the object's pose stationary relative to the Panda tip while it is grasped, and the object is released from this relationship when it is returned to its original place in the Scene Hierarchy tree upon the re-opening of the gripper. This can even be noticed in the animation of empty_container above; see the objects labeled 'Shape' followed by a number dissapear from view in the scene hierarchy when grasped (in actual fact they are still there, but are moved to into a node of the Panda's collapsed sub-tree) and re-appear when dropped. + +Note then that what matters for grasping, and for the entire task, is not that every physical property of every object matches exactly what it would be in real life, but that succesfully completed demos behave in such a way that a model could theoretically learn how some of these common items would behave in common tasks. In practice it is okay for a small item that might have a mass of 0.1kg in real life to have its mass set to 0.5 or 5kg in the simulation, as long as the task environment is reflective of the actions and interactions that would be happening in real life. Some objects, like the hanger in the *move_hanger* task, will have a certain 'visual' object as a child of a similar, more simple 'physical' object that is set to invisible (by inverting its camera visibility layers - more on this later) in order to speed up the task. This is done because having every object in a simulation be either a simple, convex shape or a grouping of such massively simplifies the calculations performed by the physics engine, helping to eradicate buggy and unrealistic behaviours which you may otherwise encounter. + +Workarounds are sometimes useful, and do not necessarily impact the quality of the task and its contribution to the data set. For example, when creating the peppers in weighing_scales, it was found that they would naturally slide towards one direction when left alone on a flat surface, as if that surface was somehow tilted. Adding an 'invisible' block to the base of the peppers increased their surface area and stopped this from happening without affecting the quality of either (a) the visual information available to the panda throughout the task or (b) the actions the Panda would take to succesfully complete it. You will likely encounter unrealistic and frustrating behaviors of objects in the physics simulator at some point - look at existing tasks to inspire creative workarounds that maintain the high quality of this dataset. + +## 1 Creating the Scene in V-Rep +## Creating the Large Container +First we will create the large container by creating 5 seperate cuboids, one for each plane of the container, then grouping these cuboids such that they can be treated as a single shape in both the V-Rep instance and the Python file. Create a cuboid with by clicking Add -> Primitive shape -> Cuboid and setting its dimensions as follows: + + + +Hit enter/okay and rename this cuboid *large_container0*, make it a child of the *empty_container* dummy in the scene hierarchy, then drag it up to or place it on the table's surface. Now we will change its color to grey by double clicking on its name in the scene hierarchy to open the Scene Object Properties; click on *Adjust color* to open the Shape dialog, then select *Ambient/diffuse component* and adjust the parameters as seen below. Note that as RGB and HSL are simply two parametizations of an item's color, only one of them needs to be set. The other one will adjust itself automatically. + + + +Next we will create the sides of the container. Add another cuboid with dimensions {*3.00e-01*, *6.00e-03*, *1.5e-03*} and open its Rotation/Orientation dialog by selecting the cuboid in the Scene Hierarchy, clicking on the Rotation icon in the upper left hand side of the window, and setting its orientation (relative to the world) as below. + + + +Once you have done that, rename it to *large_container1* and make it a child of *large_container0* in the Scene Hierarchy, then move up to the table top it by opening the mouse translation dialog, selecting mouse translation, ensuring that only the *along z* box is ticked, and dragging it it by holding the left mouse click. Remember that more precise mouse translation can be achieved by holding shift whilst dragging the object once the left mouse button has been held down. Then place it such that its edge is adjacent to the edge of the container base by performing a mouse translation whilst only *along Y* is selected. You should now have a Scene and Scene Hierarchy which looks like the following: + + + +Now we need to create and postion the rest of the large container's walls. Copy and paste the wall by selecting *large_container1* in the Scene Hierarchy and doing Ctrl+C then Ctrl+V, this will create *large_container2* which you must then make a child of the task dummy. Now we will rotate the object by performing a mouse rotation: ensuring *large_container2* is selected, open the rotation dialog, and select *Relative to: World* and *about Z*. Holding down the left mouse click, drag the mouse around until the wall has rotated 90 degrees anti-clockwise like so: + + + +We will now use a trick to place the second wall, which will work so long as *large_container1* has been perfectly centered with *large_container0* along the Y Axis (ensure this is the case by checking that the position of *large_container1* relative to its parent frame is 0.00 in the X-coord box). We can simply take make *large_container2*'s X-coord relative to its parent frame the negative of *large_container1*'s relative Y-coord position, and then set *large_container2*'s relative Y-coord position to that of *large_container1*'s relative X-coord position like below. Your *large_container1* might not have the exact same relative X and Z co-ordinates and that's perfectly okay, what matters here is that the described relationships hold: + + + +Repeat this process to create and postion the remaining two walls, working out how to position the walls for yourself. This should leave you with the following: + + + +Now we will group each of the large_container objects together in order to end up with a single object which we can manipluate and reference. While holding down the Shift key, select each of the large container objects, ensuring that you click on *large_container0* last (this is done because the created group will be names after the object last clicked on), then right click on the grouping in the Scene Hierarchy. Select Edit->Grouping / Merging->Group selected shapes like so: + + + +Rename the resulting grouped object to *large_container*. Set all 4 of its *Object special properties*, and ensure that it is set to to *Respondabl* but not *Dynamic* by clicking on *Show dynamic properties* under the *Common* tab of the Scene Object Properties dialog. + +#### A note on Grouping and Merging +Both grouping and merging allow us to treat a collection of objects as a single object, but there are some key distinctions between the two actions which will affect how we choose which action to use. As previously mentioned, the physics engine runs far faster and smoother when all shapes for which it makes calculations are convex. If a *merge* would result in a non-convex object being created, potential problems arise. Merging does, however, have the advantage of creating a tighter coupling between objects for the purpose of dynamic motion, ie motion that occurs as a result of physical laws like gravity as opposed to motion that is the direct result of scene manipulation, such as if the Panda were to grasp and move an object. This means that any grouping of objects will not preserve the relative poses of the grouped objects. For example, if we had instead created a mug object by grouping a mug body and handle and then attempted to pick up the mug by its handle, it would come detached from the body, as if they were never actually connected. In this case a merging would result in a non-convex shape, so we could instead make the mug body a child of the handle and hope that the body never has a force applied onto it that is not also applied to the handle (the mug body remains stationary relative to the handle when the handle is moved because of parent-child relationship but the reverse relationship would not hold!), or better yet find a way to create the mug as a single convex object (more on this in another document). + +That said, these problems only apply if such objects are made *dynamic*, only then can external forces other than the actions of the Panda and forces resulting from an object collision act on our objects in the V-Rep simulator. All containers to be added in this task will have their dynamic properties turned off, meaning that their pose will stay static unless an object in the scene were to do work on it, which it will not. You will however, need to ensure they are set to *Respondable*. As such, either grouping or merging would work just fine here and only actually serve the purpose of allowing us to treat the container as a single object in both the V-Rep scene and the Python File. + +#### Creating the Target Containers +Now that you have learnt how to create and compose container object from simple shapes, it is left up to you to create the two smaller target containers seen in this walthrough's first gif animation, which should be named *small_container0* and *small_container1*. The only differences between the large and target containers are (1) the targets' walls should have their *Alpha* orientation value set to 90 rather than 55 degrees once instantiated, (2) they need to be postioned slightly to either siide of the large container, and (3) they will be slightly smaller. Their absolute size does not matter too much, but they will need to be large enough to hold the collection of procedural objects spawned in the task. Finally, it is recommended that you first create one target container, position it to the side of the target container, then and copy and paste it, making sure to position it exactly on the opposite side of the large_container. The color of these target_containers will be set dynamically in the python file so you do not need to worry about setting it here. Set their *Object special* and *Dynamic* properties. Once you have completed this step, your scene should appear as below: + + + +#### Adding Proximity Sensors +We now need to add 2 proximity sensors, and they need to be positioned such that they can detect all objects placed inside the target containers. Add a *Pyramid type* proximity sensor and change its volume parameters (by clicking on *show volume parameters in its Scene Object Properties dialog* such that all X and Y fields are equivalent to the X Y dimensions of your target container's base, and its *Range* set to the height of your target container's walls. Remember to set the *Offset* parameter to 0 like so, and remember also that your volume parameters may differ slightly from the ones shown here because your target container volume will likely not be the exact same as the ones created here: + + + +Once you have added this, name it *success0*, make it a child of *small_container0*, and set its position relative to its parent to 0 in all fields. Copy and paste this sensor to create *success1*, and again make it a child of *small_container1*, setting its relative position to 0. + +#### Adding Waypoints +The last step we need to do is create waypoint dummies. This task requires 4 waypoints: + +* **waypoint0**: the approach waypoint, positioned at the center of *large_container*'s base and roughly 20 cm above it. +* **waypoint1**: the grasping waypoint. This waypoint's position will be set dynamically in the python file every time a new object needs to grasped so its position does not matter too much; here it has been placed halfway between *large_container*'s base and *waypoint0*. +* **waypoint2**: the lifting waypoint. The purpose of this waypoint is to raise objects up to a reasonable height such that the panda can then move objects horizontally without colliding into any of the objects we have created. We will positon this waypoint in the exact same location as *waypoint0*. +* **waypoint3**: the drop waypoint. The panda will release items from its grasp when this waypoint is reached and we will place this waypoint above the center of either *small_container0*. Depending on the variation, the python file will sometimes place this waypoint above *small_container1*. This dummy will have the same height as waypoints 0 and 2. + +Begin by adding a dummy and naming it waypoint 0. Set its position as described above (make the dummy a child of *large_container* and set its relative positional vector to {0, 0, 0.17}), and adjust its orientation (relative to the world) as to {180, 0, 180}, naming it *waypoint0*. One thing to be aware of is that sometimes V-Rep will struggle to set a position or orientation to absolute 0, instead setting an arbritratily small value as below: + + + +Next, copy and paste this waypoint, lowering its position by any means you wish to use to roughly halfway between *waypoint0* and the table's surface. After this copy and paste *waypoint0* again to create *waypoint3*, and leave it as is. Finally, do one final copy and paste to create *waypoint4*, and in the same way that we set the position of *waypoint0*, position it centrally over *small_container0*. Then if not already so, ensure that these waypoints are all children of large_container. Once you have done this, your scene and scene hierarchy should look like the following: + + + +#### Extension Strings +We must assign extension strings to some of the waypoints in order to control grasping and ungrasping. In order to do this, we will need to open the Scene Object Properties dialog for dummies for which we wish to add such strings, select the *Common* tab at the top of the dialog, and typing in the strings ourselves in the *Extension String* text box found under *General properties*. We want to make sure that the gripper is open and ready to grasp once it reaches *waypoint0*, as such set its extension string like so: + + + +We will set *waypoint1*'s extension string to *close_gripper()* in the same manner described above. Here we will also need to account for two seemingly contradictory factors: (1) some of the procedural objects will be spawned in such a manner where they occlude or are in contact with one another and as such grasping one would require colliding with the other and (2) path planning as we have used it so far only calculates paths if collisions can be avoided. Luckily, an extension string *ignore_collisions* exists which instructs the path planning algorithm to do just that. We can assign multiple extension strings to a single waypoint separated by a comma or semi-colon like so (do this for your *waypoint1*): + + + +Note here that the waypoint succeeding any waypoint with the *close_gripper* extension must have *ignore_collisions* set. As such we will add this to *waypoint2*, and add *open_gripper()* to *waypoint3* in order to instruct the panda to drop items into the small container. + +#### Adding the Spawn Boundary +The final step in completing our task model is to add a spawn boundary so that we can instruct the dynamic spawning of procedural objects in the python file like the one below: + + + +The simplest way to do this is probably by copying and pasting the workspace and aligning its' center with that of the *large_container* by setting it as a child of *large_container* then setting its relative pos to 0. Perform a mouse translation along the z axis such that the plane is vertically positioned roughly halfway between the base and the upper most edge of the container, then renaming it to *spawn_boundary*. Next we will adjust the size of the plane so that it is just a little smaller than the size of *large_container*'s base. Open *spawn_boundary*'s geometry dialog by clicking on *View/Modify geometry* in the *Scene Object Properties* dialog under the *Shape* tab, unchecking *Keep proportions*, and adjusting the X and Y fields under *Bounding box size*. Keeping the Z value constant, use trial and error to set values which make *spawn_boundary* a square plane just smaller than the base of *large_container*. Finally, make *spawn_boundary* a child of large container as shown in the image above. + +To finish this step, we will invert *spawn_boundary*'s visiblity layers so that it will not be visually present in the task. In general, this is done by opening the *Scene Object Properties* dialog, opening the *Common* tab, and under *Visibility* unchecking all the boxes in the first row and checking the equivalent boxes in the row just beneath it: + + + +Make sure to save the .ttm by inputting 's' in the task_validator when simulation is not running: + + + +We are now ready to begin coding the Python file. + +## 2 Writing the Python File +First we need to ensure that we import the necessary classes like below. In practice, this is normally done as you write the script, but we can do it all in one go for the purposes of this walkthrough: + +```python +from typing import List, Tuple +from rlbench.backend.task import Task +from rlbench.const import colors +from rlbench.backend.task_utils import sample_procedural_objects +from rlbench.backend.conditions import ConditionSet, DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +import numpy as np +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.dummy import Dummy +``` + +### init_task() -> None: +Here we will first instantiate object handles, remembering that the handle to a spawn boundary shape must later be passed as a list when instantiating a *SpawnBoundary* object. + +```python +self.large_container = Shape('large_container') +self.target_container0 = Shape('small_container0') +self.target_container1 = Shape('small_container1') +self.spawn_boundaries = [Shape('spawn_boundary')] +self.success_detector0 = ProximitySensor('success0') +self.success_detector1 = ProximitySensor('success1') +``` +We will then call two functions, *register_waypoint_ability_start()* and *register_waypoints_should_repeat()* like so: + +```python +self.register_waypoint_ability_start(1, self._move_above_object) +self.register_waypoints_should_repeat(self._repeat) +``` + +The first of these simply allows us to have the task execute some kind of code or functionality before moving towards this waypoint, in this case we will be defining *_move_above_next_object* which will move *waypoint1* (the grasping waypoint) to the center of the next procedural object which we will attempt to pick up. *register_waypoints_should_repeat* allows us to define for ourselves the conditions on which the panda will continue to cycle through the waypoints in order, this will allow us to tell the panda to repeat its actions until all objects have been dropped into the target container. + +### init_episode(self, index: int) -> List[str]: +Upon initialisation of every episode, we will set *self._variation_index* so that we can access it from other member functions. + +```python +self._variation_index = index +``` +Then we will sample the procedural objects found in the *rlbench/backend/assets/procdural_objects* directory by calling a function from *rlbench/backend/task_utils.py*. As can be seen in this function's definition, we need to pass it a handle to the task_dummy and the number of objects which we wish to spawn. Note that because this call takes place in *init_episode()*, we are sampling a different set of 5 items with each episode call. We will also also declare a variable *bin_objects_not_done* to hold a list of handles to objects not yet emptied out from *large_container*, this will be used later to determine where to set the subsequent positions of the grasping waypoint before each grasp. This snippet ends by registering the sampled objects as graspable, necessary to enable the grasping mechanism explained previously: + +```python +self._variation_index = index +self.bin_objects = sample_procedural_objects(self.get_base(), 5) +self.bin_objects_not_done = list(self.bin_objects) +self.register_graspable_objects(self.bin_objects) +``` + +After having done this, we will dynamically spawn our procedural objects in the scene by instantiating our *spawn_boundary* object, and using a for loop to spawn each object. For each object, this will require performing the following operations: + +* Placing each object 20cm above *large_container*'s center, then +* Sampling each object within the *spawn_boundary* in order to drop each object at a random position within the container. + +Do this like so: + +```python +b = SpawnBoundary(self.spawn_boundaries) +for ob in self.bin_objects: + ob.set_position( + [0.0, 0.0, 0.2], relative_to=self.large_container, + reset_dynamics=False) + b.sample(ob, ignore_collisions=True, min_distance=0.05) +``` + +Next we will create a handle to the release waypoint, and define a list representing a vector of its target position over *small_container0* relative to *large_container*. The X and Y vectors were obtained by temporarily making this waypoint a child of the large container and reading off its position relative to its parent in the *Position/Translation* dialog, as such your X and Y values might differ slightly from the shown here. The Z vector is simply set to +17cm, the desired relative height of *waypoint3*. + +```python +target_waypoint = Dummy('waypoint3') +target_pos = [-5.9605*10**(-8), -2.5005*10**(-1), +1.7*10**(-1)] +``` + +The next few code snippets will deal with selecting and setting the colors of each of the small containers and setting the position of the release dummy *waypoint3*, as well as the registering of success conditions. All of these will depend on the variation (and thus the specific variation index) and so will require first (1) selecting the appropriate parameters and (2) using them to achieve the above points. + +We begin by creating an empty list to later hold our success conditions, then choosing the the colors for both the target container and the other distractor container. The color of the target is chosen by simply using *index* to cycle through the const list of colors found in *rlbench/const.py*. We will select the color of the distractor at random by calling *np.random.choice()* on a list containing the indexes of *colors* which excludes the color index of the target, then selecting the chosen color using this index value: + +```python +conditions = [] +target_color_name, target_color_rgb = colors[index] +color_choice = np.random.choice( + list(range(index)) + list( + range(index + 1, len(colors))), + size=1, replace=False)[0] +_, distractor_color_rgb = colors[color_choice] +``` + +Now we will use a two-pronged if statement to set the color of the *small_container*s, register success conditions, and then select the correct position for *waypoint3*. To determine which container will be the target, we will simply alternate between them by using *index % 2 == 0* to choose *small_container1* and *index % 2 == 1* to choose *small_container1*. + +The first part of the if statement thus sets the sets *target_color_rgb* and *distractor_color_rgb* to be the colors of *small_container1* and *small_container2*, respectively, then appends a *DetectedCondition* to the *conditions* for each object. These conditions simply define a succesful task condition as all of our sampled procedural objects being detected the the proximity sensor covering the volume of our target container, *small_container0*. As the target pos of *waypoint3* relative the *large_container* is already correctly defined above, we do not have to alter this. This is written as so: + +```python +if index % 2 == 0: + self.target_container0.set_color(target_color_rgb) + self.target_container1.set_color(distractor_color_rgb) + for ob in self.bin_objects: + conditions.append(DetectedCondition(ob, self.success_detector0)) +``` + +The second prong of our if statement will do almost the same, except the variables containg rgb values to be assigned to each of the small containers will swap, and we will register the proximity sensor corresponding to the other container. We will also set *waypoint3*'s target position relative to *large_container* by changing the sign of the Y value so that it can be placed equivalently on the opposite side of the above the *large_container*, which will result in the release waypoint being placed 17cm above the center of *small_container1* if both small container's are placed perfectly on each side of the large one: + +```python +else: + self.target_container1.set_color(target_color_rgb) + self.target_container0.set_color(distractor_color_rgb) + for ob in self.bin_objects: + conditions.append(DetectedCondition(ob, self.success_detector1)) + target_pos[1] = -target_pos[1] +``` + +Next we will set the position of the target_container and register our chosen success conditions as so: + +```python +target_waypoint.set_position(target_pos, + relative_to=self.large_container, + reset_dynamics=True) +self.register_success_conditions( + [ConditionSet(conditions, simultaneously_met=True)]) +``` + +Note that here we are registering our success conditions as a *ConditionSet* object. When *simultaneously_met* is set to *True*, this results in identical functionality to simply registering a list, but if we wanted to we could alter the parameters of the *ConditionSet* call to define successful task completion as different conditions met in a certain order, or as conditions which only have to have been met at some point throughout a demo. We conclude *init_episode* by returning an exhaustive list containing the as many differing semantic descriptions of this task as we can think of: + +```python +return ['empty the container in the to %s container' + % target_color_name, + 'clear all items from the large tray and put them in the %s ' + 'tray' % target_color_name, + 'move all objects from the large container and drop them into ' + 'the smaller %s one' % target_color_name, + 'remove whatever you find in the big box in the middle and ' + 'leave them in the %s one' % target_color_name, + 'grasp and move all objects into the %s container' + % target_color_name] +``` + +### def variation_count(self) -> int: +As stated above, the set of possible variations for this task contains each possible color assignment for the target container for each possible target container. This means that we will simply return the following here: + +```python +return 2*len(colors) +``` + +### def cleanup(self) -> None: +As explained in the first walkthrough, state in the V-Rep scene must be preserved between demo runs. As such, any objects instantiated dynamically must be removed from the scene before another demo can be called. This applied to our procedural objects so will loop through them and remove them here. Because we resample a different set of 5 objects with every episode, we will also assign an empty list to *bin_objects* so that it can work with subsequent demo runs: + +```python +def cleanup(self) -> None: + if self.bin_objects is not None: + [ob.remove() for ob in self.bin_objects if ob.still_exists()] + self.bin_objects = [] +``` + +### def step(self) -> None: +We will use the step function to handle the list of objects not yet correctly placed. As such, this list should reflect the collection of precedural objects not currently detected by the success detector. As it is not possible to assign a handle to an object handle and then call V-Rep API functions (functions dynamically affecting or reading the state of the V-Rep scene whilst a demo is running) on such handles, we will again use an if statement conditional on *_variation_index % 2* to select the handle to the appropriate proximity sensor. We can itereate through the list of procedural objects which still need to be removed from *large_container* and call *.remove()* to remove them from said list like so: + +```python +for ob in self.bin_objects_not_done: + if self._variation_index % 2 == 0: + if self.success_detector0.is_detected(ob): + self.bin_objects_not_done.remove(ob) + else: + if self.success_detector1.is_detected(ob): + self.bin_objects_not_done.remove(ob) +``` + +### def \_move_above_object(self, waypoint): +This is the function to be called just before any movements/actions are made towards a specified waypoint. We registered this function in *init_task()* and have already passed the grasping waypoint *waypoint1* to to the registering. As such, it is called as soon as *waypoint0* has been reached and any actions specified through extension strings or other means have been performed. We want it to set *waypoint1*'s position to objects which are yet to be removed from *large_container*, as such we will use a getter function to retrieve the position of the first element of *bin_objects_not_done* (this will actually retrieve the position of the central point of an object's bounding box) and then use this to specify *waypoint1*'s position: + +```python +if len(self.bin_objects_not_done) <= 0: + raise RuntimeError('Should not be here.') +x, y, z = self.bin_objects_not_done[0].get_position() +waypoint.get_waypoint_object().set_position([x, y, z]) +``` + +### def \_repeat(self): +The final function to be written simply needs to return a boolean value specifying whether or not we want to repeat the path and actions specified by the waypoints: *True* if objects still need to be placed in the target container and *False* otherwise. We could read the state of the success detector as we did in *step()* but seeing as we already have list representing the objects yet to be moved we can perform a simple comparison: + +```python +return len(self.bin_objects_not_done) > 0 +``` + +We have now finished creating the scene and python files for this task. Cycle through episodes and run a few demos for a few variations to verify that the task behaves as described throughout this walkthrough. If the task behaves in an unexpected way, or if it fails to succesfully complete a majority of demos, you could play around with the task by playing around with the height of waypoints. The task has been quite narrowly defined throughout this walkthrough, but for tasks you create in general you will likely have to play around with the positions and orientations of waypoints and other objects, their dynamic properties like their massess, extension strings etc, as well as the functions (and parameters) you call in your python file and where you call them from. If your task still fails to achieve the intended behaviour, you may have to redesign the task by remodelling objects and re-thinking your waypoint strategy in the V-Rep simulator, as well as perhaps simplifying your task specification or implementation to reduce its complexity and better handle edge cases. The RLBench and PyRep code bases are your friends here; both provide a wealth of member functions which you can familiarise yourself with by searching through the backend and other files in both repositories, as well as by looking at other tasks which implement functionaility similar to that which your tasks might require. In particular you will want to be aware of the different varieties of success conditions you can register in *rlbench/backend/conditions.py*. + +## 4 Passing the Task Validator +Before your task is complete, every variation must pass the task_validator. For tasks like the one we have created here which have 20 variations which are all almost identical to each other, it is usually sufficient to verify they work for at least 3 variations. + +## 5 The Open Road +Congratulations, you have now completed both tutorials on creating tasks for RLBench. Any tasks you choose to create and add to this dataset will be making a contribution to the research surrounding Machine Learning in Robotics so any input will be greatly appreciated. This is where the fun starts; you can now start thinking creatively about designing your own tasks and you are now armed with the tools you need to build them. diff --git a/third_party/AnyBimanual/third_party/RLBench/tutorials/simple_task.md b/third_party/AnyBimanual/third_party/RLBench/tutorials/simple_task.md new file mode 100644 index 0000000000000000000000000000000000000000..01c3b9b6e22a02ed37ea907f9bcda59eef52e94d --- /dev/null +++ b/third_party/AnyBimanual/third_party/RLBench/tutorials/simple_task.md @@ -0,0 +1,214 @@ +# Simple Task Walkthrough + +This is a walkthrough of one of the entire task building process of one of the simpler tasks: slide_block_to_target. +The task aims to make the Panda robot slide a cube to a specific target location and if done right you will end up with something like the following: + + + +Note that creating a task involves (1) creating the task through the task builder, (2) creating the task by adding objects to the scene in a V-Rep simulator instance, then (3) coding the Python File and finally (4) passing the Task Validator. The last two steps are often an iterative process depending on the complexity of the task. (2) Will require first physical objects to be added, then normally sensors used to detect objects and thus define a succesful task completion, then waypoints used in calculating the Panda Robot's path and action in the simulator. (3) Involves creating handles to various objects and defining what counts as succesful task completion. (4) Must be passed for a task to be deemed a sufficient addition to this data set. + +The design of a task must be given careful consideration. Ideally they would be tasks which one can imagine a Panda Arm Robot might one day be asked to do in real life; many tasks built thus far are common household ones such as hanging a picture frame on a wall peg or setting the dinner table, but tasks from construction, manufacturing and any general labour intensive sphere would also be seen as useful. Tasks also have varying levels of complexity and often have common themes tying them together; a good example would be open_cabinet and take_cup_out_from_cabinet. All that said, there really are no hard restrictions so use your creativity and imagination to see what you can create. + + +## Variations and their Episodes +At this point it is worth talking about episodes and variations. Every task has various textual descriptions asscosciated with them, to be used for linguistic learning in robotic tasks if desired. The picture below shows the various task descriptions returned from the init_episode() function in task *toilet_seat_up* for its single variation. Ideally these will between them include all possible ways of using the English language to describe a task; for example two of the various descriptions given for a particular episode of stack are 'stack the other cups on top of the red cup' and 'pick up and set the cups down into the red cup'. + + + +Every task has at least one variation, and every variation must have multiple episodes. *stack_cups* has multiple variations for each color of the base cup, for example another one of its variations could be described as 'stack the other cups on top of the blue cup'. Variations aren't restricted to varying colors either; setup_checkers has three episdoes, each instructing the Panda to setup the remaining 1, 2, or 3 checkers on the board. + +A *variation* is thus defined as a specific semantic meaning of the textual description of the task, such that different variations actually instruct the robot to do something slightly different on every variation. An *episode* is defined as a specific physical starting state (most likely layout/positioning) of the task objects for a particular variation, and these must differ between episodes across the same variation with the same semantic description. At minimum, the positioning/orientation of task objects must vary with episodes, but many tasks built so far vary object colors and sucess conditions (alongside Panda action instruction) with every change in episode. + +## 1 Start the task builder +First off, simply run the task builder. A new V-Rep instance should automatically open, and this will run from the code found in the RLBench backend and Pyrep repos. + +You will then be prompted to enter which task to edit. In this case, enter 'slide_block_to_target' as the task name, press enter, and enter 'y' to when asked to confirm if you want to create a new task. This will prompt the task builder to create both a python file in *rlbench/tasks* and a corresponding scene file with a .ttm extension in *rlbench/task_ttms*. Shown below is the task_buidler dialog for creating a new task called slide_block upon starting the task_builder. + + + +The ttm file should load automatically in the V-Rep instance, and this will contain the scene (a collection of walls, cameras, a table/workspace boundary, and the panda robot) and a task dummy which should in this case be named 'slide_block_to_target'. Shown below is the empty scene task_design_empty.ttt containing the empty task_dummy object named 'complex_task' for a task also named 'complex_task'. + + + +Notice in particular the Scene Hierarchy on the left, showing the parent child relationships for all existing objects in the scene, presented in a tree structure. Two things to note here: (1) Be aware that moving/rotating an object will do the same to its child objects, treating them as one single object for these particular operations and (2) it is important that any V-Rep objects created for the task must be made children of this task dummy, and that any objects that exist across tasks as part of the empty scene file (.ttt) are not included in the task dummy's downwards tree. This will ensure that when we save the task it is saved only to the task's scene file (.ttm), and that when we change between tasks everything is cleared away and loaded appropriately. To change the ordering within the scene hierarchy, simply hold down the mouse click and drag the object names up or down to the desired new parent and then release the mouse click. + + +## 2 Creating the Task in V-REP +To create a task, it must first be designed in the RLBench V-Rep instance. We will create the needed objects, making sure to set the dynamic and object special properties. For this task, a cube cube named 'block' was added along with a plane named 'target'. These objects need to placed on top of the table, within the 'workspace' boundary in order to make sure the panda can reach the objects and complete the task. + +Note first that in order to pan your view in the V-Rep instance you can make sure that the pan icon is selected in the upper left section of the screen (see picture below) then pan your view in a drag and drop fashion. To rotate your view angle, either hold down the scroller wheel button on your mouse if available, or select the icon directly to the right of the camera pan icon and move it in a drag and drop fashion. Spend a few seconds getting comfortable with this as creating a task will involve needing to view various positions from all sorts of angles. + + + +To create the block, go to the top toolbar and click on Add -> Primitive Shape -> Cuboid. You will then be prompted with a dialog where you can initialise the cuboid's properties: + + + +For now let's ensure the cuboid dimensions are 0.1m along each edge, and click ok to create it. Notice that this cuboid is grey in color and is spawned on the floor underneath the table. Seeing as we will be setting the colour of the block dynamically from the Python file side there will be no need to do that just yet, but be aware that we would normally adjust the color of created objects as shown below. This is done by double clicking on the Cuboid in the V-Rep scene heirarchy to open the Scene Object Properties dialog, ensuring we are viewing the 'Shape' rather than 'Common' Properties, and clicking on either 'Adjust color' or 'Adjust texture'/'Quick textures (selection)'. + + + +Once our cuboid has been created, we need to place it on top of the table. There are multiple ways to do this, here we will simply drag and drop it. In order to be able to to do this, we need to select the Translation/Position dialog by clicking on its icon in the upper left section of the V-Rep instance. We can select multiple ways of controlling an objects position; to select drag and drop click on the 'Mouse Translation' tab. From here we can select the axes along which to translate, selecting only 'along Z' as this will allow us to adjust its vertical position as shown in the figure below. By dragging it upwards we can place it on top of the table, noting that it is not a problem for there to be a (very) miniscule gap between the object and the table. To reduce the step size of the translation, initiate the translation by holding down the left mouse click, then keep the shift key pressed as this will allow you to place the object more precisely. Note also that to move objects along other axes or planes we can simply select the desired axes of translation as so, and that sometimes we want to move objects along axes relative to other objects if thieir bounding box has an awkward orientation (more on this in another tutorial). + + + +We finish creating the cuboid by setting its *Object Special Properties*. Double click on the cuboid in the Scene Hierarchy to open its Object Special Properties Dialog and open the *Common* tab. Under *Object special properties* set the object to *Collidable*, *Measurable*, *Detectable* and *Renderable* by checking their respective boxes: + + + +*Collidable* means that the V-Rep simulator is able to detect a collision with other objects, and this is particular useful for path planning. *Measurable* enables minimum distance calculation between two objects and this we normally want to set this to enabled. *Detectable* enables detection of the object by proximity sensors; we will add one later in this task. Dinally *Renderable* means the object can be rendered by cameras and we also want this enabled for almost every object so that an object is visually present at training time. + +Next we will create the target_visual. Create a plane by clicking on Add -> Primitive Shape -> Plane and create a plane with sides of length 0.1m. Set all four of its *Object special properties*. As we have done for the cuboid, drag and drop this onto the surface of the table, placing it next to the cuboid with roughly 0.1m of seperation. In this example, the cuboid is colored red and the plane is white, but you only need to worry about giving the plane a color so that it can be rendered as a surface and not just a hollow collection of edges, also known as a wireframe. At this point you should have something similar to the following picture: + + + +We colored in our plane white, but only parts of the plane appear colored from most viewpoints, the rest are not visible. You will notice that if the plane has the same vertical position as the surface of the table, it may or may not be wholly visible depending on the angle it is viewed from. For this reason, we will use the translation dialog to increase its z height by a miniscule amount. Open the Translation/Position dialog and go the 'Translation' tab. Enter *+1.00e-5* in the *Along Z* box and click on the *Z-translate sel.* button. Your plane should now be placed just above the surface of the table, and its colored surface visible from all direct viewpoints. + + + +Now that we have the necessary physical objects placed appropriately in the scene, we need to add a proximity sensor. This will be needed to detect if the block has reached the target and will be used to define a succesful task completion later when writing the python file. Click on Add -> Proximity Sensor -> Pyramid Type. This will create a sensor with a detection volume far bigger than we need, so double click on the Proxity_sensor in the Scene Hierarchy to open its Scene Object Properties, click on *Show volume parameters* to open the Detection Volume Parameters dialog, and fill the fields in as shown below. This will drastically reduce the size like so: + + + +We want the task to slide the cuboid to the plane, as such we will place the proximity sensor at the centre of the plane. The easiest way to do this is to first make the sensor a child of the plane: drag and drop it onto the plane in the scene hierarchy, open the Translation/Position dialog, click on the *Position* tab, and then set its position relative to its parent frame to 0 along all axes like below. Rename this sensor to *success*. + + + +Next we need to deal with the physical properties of our objects. Both the block and the target have all Object Special Properties (Collidable, Measureable, Detectable, Renderable) set, and the block needs to be set to both respondable and dynamic within the dynamic properties log. *Respondable* means the object has a physical presence, ie that it exists for the purposes of physical interaction with other objects in the scene. *Dynamic* means external forces other than those applied to it by other forces can act on it, ie gravity and reactionary force. For both block, open its Scene object properties dialog, ensure the *Shape* tab is open, then click on *Show dynamic properties dialog*. This will open the Rigid Body Dynamic Properties dialog, check the boxes correspnding to *Body is respondable* and *Body is dynamic*. + +Next, dummies named *waypoint0* and *waypoint1* need to be added. Dummies named in this way act as much needed waypoints in defining the intended motion and action of the panda gripper, and will be used by the path planning script in RlBench to determine what the panda does in simulation. The intended orientation of the dummies must be set appropriately, and in the next tutorial it will be seen that dummies can have extension_strings assosciated with them, instructing the gripper to do more complex things like grasping and ignoring collisions in path planning. Note that the panda gripper's motion and action will follow first waypoint0, then waypoint1 and so on in an ascending order. + +To add such dummies, create them by clicking on Add -> Dummy. Make it a child of the Proximity Sensor and set its position relative it its parent frame to 0 along all axes, renaming this dummy as 'waypoint0' by double clicking on its name in the Scene Hierarchy, typing it, and and hitting enter. Add another Dummy and set it to a child of the cuboid, then open the Translation/Position dialog, set its relative position (to parent) to 0m in the X-coord and Z-coord boxes, but set the Y-coord box to -0.1m such that it is placed on the other side of the cuboid as the plane and proximity sensor. Name this 'waypoint1'. + +At this point, we have a block sitting between 'waypoint0' and 'waypoint1', and hence Panda ends up planning a path that goes around the block. To avoid this, we must assign extension strings to 'waypoint1' by opening its Scene Object Properties dialog, select the Common tab at the top of the dialog, and typing in 'ignore_collisions()' in the Extension String text box found under General properties. +Finally we need to set the orientation of the waypoint dummys so that the panda has the easiest possible path to plan; this is because in simulation the panda will orient its gripper such that the dummy named 'Panda_tip' (which maintains constant pose relative to the Panda Tip) has an identical orientation to the waypoints it is reaching, and care must be taken to ensure that these waypoints have a feasible (ie reachable) pose. Open the Object/Item Rotatation/Orientation dialog by clicking on the icon just to the right of the Position/Translation dialog, go the the *Orientation* tab, and fill in its orientation (relative to the world) like so: + + + +Finally, a plane named 'boundary' was created. This is defined to randomly spawn (move and rotate) the task objects within itself, and is needed for when we code the python file later. To create this we could simply add a plane of desired geometry and drag it onto the table surface, but there is an easier way. Select the workspace in the Scene hierarchy, do Ctrl+c then Ctrl+v to copy and paste it, then double click on the newly created workspace0 in the Scene Hierarchy to open its Scene Object Properties dialog. Click on *View/modify geometry* to open its geometry dialog, uncheck the *Keep proportions* box, and change the X and Y properties under *Scaling factors* to 0.70, keeping Z as 1.00. Click apply; this should leave you with a smaller *workspace0* inside than the workspace boundary, and take care to ensure that all objects which we have created are positioned within this new boundary. + + + +Remember to make *workspace0* a child of the slide_block_to_target task dummy by dragging and dropping it onto the task dummy within the scene hierarchy. Now we need to rename the shapes so that we can create handles with maximum readability for them later on in the Python file. Rename the Cuboid to *block*, the proximity sensor to *success*, workspace0 to *boundary* and the Plane to *target*. Finally, ensure all objects we created are children of the target plane, as later on when we define random spawning of the plane all child objects will be keep their pose relative to their parent constant. Ensure that your parent-child hierarchy is as so: + + + +## 3 Writing the Python File +Now that we have created our simple scene file, we find and open *rlbench/tasks/slide_block_to_target.py*. This will contain empty functions as shown below, most of which must be defined at this point. + +```python +from typing import List +from rlbench.backend.task import Task + + +class ComplexTask(Task): + + def init_task(self) -> None: + # TODO: This is called once when a task is initialised. + pass + + def init_episode(self, index: int) -> List[str]: + # TODO: This is called at the start of each episode. + return [''] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 1 + + def step(self) -> None: + # Called during each sim step. Remove this if not using. + pass + + def cleanup(self) -> None: + # Called during at the end of each episode. Remove this if not using. + pass +``` + +Explained, these are: + +* **init_task()** - This is called whenever the simulator is started, and is called before any other functions contained in this file. +* **init_episode()** - This is called before the start of each indivual demo, it will automatically sample the task dummy and thus any children it has within the workspace whilst keeping the relative pose between all task objects we have created constant, but the rest is left up to the task creator. +* **variation_count()** - This must return the total number of variations, necessary for automating the running of this data set. +* **step()** - This is called at each time step in simulation, and can be used for altering the state of the scene once certain conditions have been met, for example the button in push_button changes color from red to green once succesfully pressed. This function is optional and should be deleted when not used. +* **cleanup()** - This is used to reset the state of the scene in between each run, it is called upon the completion of any succesful or unsuccesful demo. If your task alters the set of objects within the scene, ie by creating or deleting objects dynamically, the scene must be restored to its original state here. For example, in wipe_desk, the dirt spots dynamically created and spawned at the beggining of each demo must be deleted. This should also be deleted when not being used. + +We first ensure that we import the necessary functions and classes from the pyrep and rlbench backend, the ones ones listed here are typical of most tasks and include standard classes Shape and Task, as well as classes interacting with the Spawn Boundary and Proximity Sensor, and finally a list defining colours in RGB format and a definition for the success condition to be used in this task: + +```python +from typing import List +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from rlbench.const import colors +from rlbench.backend.task import Task +from rlbench.backend.conditions import DetectedCondition +from rlbench.backend.spawn_boundary import SpawnBoundary +``` + +### init_task() -> None: +Next we need to complete the functions instantiated in the SlideBlockToTarget task. The init_task() function will be called once when the simulator is started (by entering '+' in the task_builder dialog) and will need to contain our initialisation of various types of object handles, as well as the initialisation of success conditions that will remain constant throughout different episodes. Note that a demo of a task ends once all success conditions have been reached, these will include conditions on the state of the gripper (ie whether or not it is currently grapsing), as well as conditions on a particular proximity sensor detecting a particular object. More complex tasks will have success conditions varying and re-registering with each new episode and these will thus be dealt with inside the init_episode() function. + +In order to instiate an object handle, simply create it using existing classes. Most objects, including boundary planes, will be a simple Shape, but other classes like spawn boundaries and proximity sensors will need to be created as such: + +```python +self.block = Shape('block') +success_detector = ProximitySensor('success') +self.target = Shape('target') +self.boundary = SpawnBoundary([Shape('boundary')]) +``` + +The last step to create init_task for this task will be to instatiate the success condition as a DetectedCondition() class, passing first the handle to the object to be detected and the intended proximity sensor, and then these success conditions will be registered, instructing the task_builder to stop and declare a succesful task once this condition has been reached. Notice that these conditions must be passed as a list, allowing multiple conditions to be ANDed together to define succesful task completion: + +```python +success_condition = DetectedCondition(self.block, success_detector) +self.register_success_conditions([success_condition]) +``` + +### init_episode(self, index: int) -> List[str]: +If the task is to have more than 1 variation, this function must first set *self._variation_index* for as below. Be aware for when creating your own tasks that this *index* variable is the only available information pertaining to which variation the task is currently set to perform. As such, any differences in your python script execution flow between variations must be dependent on this variable. This will, however, be illustrated in a more advanced tutorial so you do not need to worry about this for now. + +```python +self._variation_index = index +``` + +On calling a new episode, RLBench backend will randomly sample the task_dummy, along with all of its children, within the workspace boundary, keeping their relative positions and orientatations constant. In order to add more task variations to the dataset, we can cycle through the coloring of objects and textual descriptions (more on this later) of tasks by having these variations depend on the index. Here we cycle through block colours by choosing the desired colour from the pre-exisiting 'colors' list and calling .set_color on the block: + +```python +block_color_name, block_rgb = colors[index] +self.block.set_color(block_rgb) +``` + +Next we clear our boundary and call .sample() on it in order to randomly move and rotate the task objects within it. Notice that only self.target needs to be passed in order to sample all task objects together whilst keeping their relative positions in tact, this is because all task objects (excluding the boundary) are children of self.target: + +```python +self.boundary.clear() +self.boundary.sample(self.target) +``` + +We finish writing init_episode by defining its return. This must be a list of task descriptions written in english, to be used for semantic learning. Notice the *%s* string placeholder, this is done because the specific value of that string (aka the block color) will vary with every variation and is determined by the self.variation_index variable seen above. Make sure to return a list containing as many textual descriptions as you can think of, as this will massively aid this dataset's usefulness to robotic linguistic learning. + +```python +return ['slide the %s block to target' % block_color_name, + 'push the %s cube to the white plane' % block_color_name, + 'nudge the %s block so that it covers the white target' % block_color_name, + 'Find the %s item on the table and manipulate its position so that it reaches the plane' % block_color_name] +``` + +### variation_count(self) -> int: +Finally, every task needs to have variation_count() defined. This must return the number of distinct task variations. In the case of slide_block_to_target, each variation will set the block colour to a different element from the colors list. As such, this function will simply return its length: + + +```python +return len(colors) +``` + +### Other class functions +Any other functions not being used, such as cleanup(), can be deleted in order to achieve a readable python file. + +## 4 Playing around with the Task +Once the scene and python files have been created, it is time to test the task runs. This can be done by entering '+' in the task builder dialog to run the simulator, then 'd' to test the demo of this particular variation. We are looking to make sure that the panda can reach most spawning positions across episodes and that the task works for all variations, this can be done by running demos for different episodes (press 'e' to cycle) and variations (press 'v') respectively. Common (but non-exhaustive) things to change at this point include changing the position/orientation/size of objects to make them reachable/graspable by the gripper, changing the position/orientation/size of any spawn boundaries to ensure most variations run smoothly, changing the min_rotation and max_rotation (through altering the .sample() parameters for any spawn boundary sampling and by redifining base_rotation_bounds() for workspace sampling on init_episode() calls), changing the dynamic properties (inc mass), and tinkering with the waypoint dummys to ensure a smooth and straightfoward path can be consistently planned. + +## 5 Passing the Task Validator +To ensure a task has been made to completion, it must pass the task validator. This can be done by passing '!' to the task_builder dialog when not in simulation, and the task validator must deem the task succesful for demos it runs across every distinct variation. Most demos must be succesfully passed for the task validator to declare a succesful task. If your task does not pass this, go back to Step 4 and try again. If this still proves to be a major challenge, redesign and recreate the task in a simpler fashion. + +## 6 What's Next? +You should now be able to understand how the simpler tasks in RlBench were created. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/third_party/AnyBimanual/third_party/YARR/README.md b/third_party/AnyBimanual/third_party/YARR/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ad6f4880edc77b151fab56169e008eab23563fac --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/README.md @@ -0,0 +1,28 @@ +![Logo Missing](logo.png) + +**Note**: Pirate qualification not needed to use this library. + +YARR is **Y**et **A**nother **R**obotics and **R**einforcement learning framework for PyTorch. + +The framework allows for asynchronous training (i.e. agent and learner running in separate processes), which makes it suitable for robot learning. +For an example of how to use this framework, see my [Attention-driven Robot Manipulation (ARM) repo](https://github.com/stepjam/ARM). + +This project is mostly intended for my personal use (Stephen James) and facilitate my research. + +## Modifcations + +This is my (Mohit Shridhar) fork of YARR. Honestly, I don't understand what exactly is happening in a lot of places, so there a lot of hacks to make it work for my purposes. If you are doing simple behavior cloning, you can probably write simpler training and evaluation routines, but YARR might be useful if you also want to do RL. Here is a quick summary of my modifcations: + +- Switched from randomly sampling evaluation episodes to deterministic reloading of val/test dataset episodes for one-to-one comparisons across models. +- Separated training and evaluation routines. +- Task-uniform replay buffer for multi-task training. Each batch has a uniform distribution of tasks. +- Added cinematic recorder for rollouts. +- Some other weird hacks to prevent memory leaks. + +## Install + +Ensure you have [PyTorch installed](https://pytorch.org/get-started/locally/). +Then simply run: +```bash +python setup.py develop +``` diff --git a/third_party/AnyBimanual/third_party/YARR/logo.png b/third_party/AnyBimanual/third_party/YARR/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..3d5895ce0632418ce5e8956feff64f83e92d1900 Binary files /dev/null and b/third_party/AnyBimanual/third_party/YARR/logo.png differ diff --git a/third_party/AnyBimanual/third_party/YARR/requirements.txt b/third_party/AnyBimanual/third_party/YARR/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..f6fabf4d2fecebeaa8a59f9611bfed1b7c0915b7 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/requirements.txt @@ -0,0 +1,11 @@ +tensorboard +moviepy +natsort +psutil +timeout-decorator +pyrender==0.1.45 +omegaconf +hydra-core==1.0.5 +pandas==1.4.1 +opencv-python + diff --git a/third_party/AnyBimanual/third_party/YARR/setup.py b/third_party/AnyBimanual/third_party/YARR/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..af8277439a8df8fda2ba0472970ae3f54b735c53 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/setup.py @@ -0,0 +1,37 @@ +import codecs +import os + +import setuptools + + +def read(rel_path): + here = os.path.abspath(os.path.dirname(__file__)) + with codecs.open(os.path.join(here, rel_path), 'r') as fp: + return fp.read() + + +def get_version(rel_path): + for line in read(rel_path).splitlines(): + if line.startswith('__version__'): + delim = '"' if '"' in line else "'" + return line.split(delim)[1] + else: + raise RuntimeError("Unable to find version string.") + + +def get_install_requires(): + install_requires = [] + with open('requirements.txt') as f: + for req in f: + install_requires.append(req.strip()) + return install_requires + + +setuptools.setup( + version=get_version("yarr/__init__.py"), + name='yarr', + author='Stephen James', + author_email='slj12@ic.ac.uk', + packages=setuptools.find_packages(), + install_requires=get_install_requires() +) diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/__init__.py b/third_party/AnyBimanual/third_party/YARR/yarr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..11d27f8c7c435c335afaa298cf06e2cc1b202e0c --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/__init__.py @@ -0,0 +1 @@ +__version__ = '0.1' diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/agents/__init__.py b/third_party/AnyBimanual/third_party/YARR/yarr/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/agents/agent.py b/third_party/AnyBimanual/third_party/YARR/yarr/agents/agent.py new file mode 100644 index 0000000000000000000000000000000000000000..0db4526de5c8511e78cc088528a61bd994d7f98c --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/agents/agent.py @@ -0,0 +1,342 @@ +from abc import ABC, abstractmethod +from typing import Any, List +import inspect + + +class Summary(object): + def __init__(self, name: str, value: Any): + self.name = name + self.value = value + + +class ScalarSummary(Summary): + pass + + +class HistogramSummary(Summary): + pass + + +class ImageSummary(Summary): + pass + + +class TextSummary(Summary): + pass + + +class VideoSummary(Summary): + def __init__(self, name: str, value: Any, fps: int = 30): + super(VideoSummary, self).__init__(name, value) + self.fps = fps + + +class ActResult(object): + + def __init__(self, action: Any, + observation_elements: dict = None, + replay_elements: dict = None, + info: dict = None): + self.action = action + self.observation_elements = observation_elements or {} + self.replay_elements = replay_elements or {} + self.info = info or {} + + +class Agent(ABC): + + @abstractmethod + def build(self, training: bool, device=None) -> None: + pass + + @abstractmethod + def update(self, step: int, replay_sample: dict) -> dict: + pass + + @abstractmethod + def act(self, step: int, observation: dict, deterministic: bool) -> ActResult: + # returns dict of values that get put in the replay. + # One of these must be 'action'. + pass + + def reset(self) -> None: + pass + + @abstractmethod + def update_summaries(self) -> List[Summary]: + pass + + @abstractmethod + def act_summaries(self) -> List[Summary]: + pass + + @abstractmethod + def load_weights(self, savedir: str) -> None: + pass + + @abstractmethod + def save_weights(self, savedir: str) -> None: + pass + + +class BimanualAgent(Agent): + """ + + """ + + def __init__(self, right_agent: Agent, left_agent: Agent): + self.right_agent = right_agent + self.left_agent = left_agent + self._summaries = {} + + def build(self, training: bool, device=None) -> None: + self.right_agent.build(training, device) + self.left_agent.build(training, device) + def update(self, step: int, replay_sample: dict) -> dict: + right_observation = {} + left_observation = {} + + for k, v in replay_sample.items(): + if "rgb" in k or "point_cloud" in k or "camera" in k: + right_observation[k] = v + left_observation[k] = v + elif "right_" in k : + right_observation[k[6:]] = v + elif "left_" in k: + left_observation[k[5:]] = v + else: + right_observation[k] = v + left_observation[k] = v + + action = replay_sample["action"] + right_action, left_action = action.chunk(2, dim=2) + right_observation["action"] = right_action + left_observation["action"] = left_action + # qattention_peract_bc_agent + right_update_dict = self.right_agent.update(step, right_observation) + left_update_dict = self.left_agent.update(step, left_observation) + + # relation between left and right + total_losses = right_update_dict["total_losses"] + left_update_dict["total_losses"] + self._summaries.update({"total_losses": total_losses}) + return self._summaries + + + def act(self, step: int, observation: dict, deterministic: bool) -> ActResult: + + observation_elements = {} + info = {} + + right_observation = {} + left_observation = {} + + for k, v in observation.items(): + if "rgb" in k or "point_cloud" in k or "camera" in k: + right_observation[k] = v + left_observation[k] = v + elif "right_" in k : + right_observation[k[6:]] = v + elif "left_" in k: + left_observation[k[5:]] = v + else: + right_observation[k] = v + left_observation[k] = v + + right_act_result = self.right_agent.act(step, right_observation, deterministic) + left_act_result = self.left_agent.act(step, left_observation,deterministic) + action = (*right_act_result.action, *left_act_result.action) + + observation_elements.update(right_act_result.observation_elements) + observation_elements.update(left_act_result.observation_elements) + + info.update(right_act_result.info) + info.update(left_act_result.info) + + return ActResult(action, observation_elements=observation_elements, info=info) + + def reset(self) -> None: + self.right_agent.reset() + self.left_agent.reset() + + def update_summaries(self) -> List[Summary]: + summaries = [] + for k, v in self._summaries.items(): + summaries.append(ScalarSummary(f"{k}", v)) + + right_summaries = self.right_agent.update_summaries() + left_summaries = self.left_agent.update_summaries() + + for summary in right_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_right/{summary.name}" + + for summary in left_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_left/{summary.name}" + + return right_summaries + left_summaries + summaries + + + def act_summaries(self) -> List[Summary]: + right_summaries = self.right_agent.act_summaries() + left_summaries = self.left_agent.act_summaries() + + for summary in right_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_right/{summary.name}" + + for summary in left_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_left/{summary.name}" + + return right_summaries + left_summaries + + def load_weights(self, savedir: str) -> None: + self.right_agent.load_weights(savedir) + self.left_agent.load_weights(savedir) + + def save_weights(self, savedir: str) -> None: + self.right_agent.save_weights(savedir) + self.left_agent.save_weights(savedir) + + +class LeaderFollowerAgent(Agent): + + def __init__(self, leader_agent: Agent, follower_agent: Agent): + self.leader_agent = leader_agent + self.follower_agent = follower_agent + self._summaries = {} + + def build(self, training: bool, device=None) -> None: + self.leader_agent.build(training, device) + self.follower_agent.build(training, device) + + def update(self, step: int, replay_sample: dict) -> dict: + + leader_observation = {} + follower_observation = {} + + for k, v in replay_sample.items(): + if "rgb" in k or "point_cloud" in k or "camera" in k: + leader_observation[k] = v + follower_observation[k] = v + elif "right_" in k : + leader_observation[k[6:]] = v + elif "left_" in k: + follower_observation[k[5:]] = v + else: + leader_observation[k] = v + follower_observation[k] = v + + action = replay_sample["action"] + right_action, left_action = action.chunk(2, dim=2) + leader_observation["action"] = right_action + follower_observation["action"] = left_action + # leader -> right + leader_update_dict = self.leader_agent.update(step, leader_observation) + import torch + follower_observation['low_dim_state'] = torch.cat([follower_observation['low_dim_state'], + replay_sample["right_trans_action_indicies"], + replay_sample["right_rot_grip_action_indicies"], + replay_sample["right_ignore_collisions"]], dim=-1) + + follower_update_dict = self.follower_agent.update(step, follower_observation) + + total_losses = leader_update_dict["total_losses"] + follower_update_dict["total_losses"] + self._summaries.update({"total_losses": total_losses}) + return self._summaries + + def act(self, step: int, observation: dict, deterministic: bool) -> ActResult: + + observation_elements = {} + info = {} + + leader_observation = {} + follower_observation = {} + for k,v in observation.items(): + if "right_" in k and not "rgb" in k and not "point_cloud" in k and not "camera" in k: + leader_observation[k[6:]] = v + elif "left_" in k and not "rgb" in k and not "point_cloud" in k and not "camera" in k: + follower_observation[k[5:]] = v + else: + leader_observation[k] = v + follower_observation[k] = v + # print(inspect.getfile(self.leader_agent.act)) + right_act_result = self.leader_agent.act(step, leader_observation, deterministic) + + right_observation_elements = right_act_result.observation_elements + + import torch + + device = follower_observation['low_dim_state'].device + if "trans_action_indicies" in right_observation_elements: + right_trans_action_indicies = torch.from_numpy(right_observation_elements["trans_action_indicies"]).unsqueeze(0).unsqueeze(0).to(device) + right_rot_grip_action_indicies = torch.from_numpy(right_observation_elements["rot_grip_action_indicies"]).unsqueeze(0).unsqueeze(0).to(device) + right_ignore_collisions = torch.from_numpy(right_act_result.action[-1:]).unsqueeze(0).unsqueeze(0).to(device) + else: + right_trans_action_indicies = torch.empty((1, 1, 3)).to(device) + right_rot_grip_action_indicies = torch.empty((1, 1, 4)).to(device) + right_ignore_collisions = torch.empty((1, 1, 1)).to(device) + + + follower_observation['low_dim_state'] = torch.cat([follower_observation['low_dim_state'], + right_trans_action_indicies, + right_rot_grip_action_indicies, + right_ignore_collisions], dim=-1) + + left_act_result = self.follower_agent.act(step, follower_observation, deterministic) + + action = (*right_act_result.action, *left_act_result.action) + + observation_elements.update(right_act_result.observation_elements) + observation_elements.update(left_act_result.observation_elements) + + info.update(right_act_result.info) + info.update(left_act_result.info) + + return ActResult(action, observation_elements=observation_elements, info=info) + + + def reset(self) -> None: + self.leader_agent.reset() + self.follower_agent.reset() + + def update_summaries(self) -> List[Summary]: + + summaries = [] + for k, v in self._summaries.items(): + summaries.append(ScalarSummary(f"{k}", v)) + + leader_summaries = self.leader_agent.update_summaries() + follower_summaries = self.follower_agent.update_summaries() + + for summary in leader_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_leader/{summary.name}" + for summary in follower_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_follower/{summary.name}" + + return leader_summaries + follower_summaries + summaries + + + def act_summaries(self) -> List[Summary]: + leader_summaries = self.leader_agent.act_summaries() + follower_summaries = self.follower_agent.act_summaries() + + for summary in leader_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_leader/{summary.name}" + for summary in follower_summaries: + if not isinstance(summary, ImageSummary): + summary.name = f"agent_follower/{summary.name}" + + return leader_summaries + follower_summaries + + def load_weights(self, savedir: str) -> None: + self.leader_agent.load_weights(savedir) + self.follower_agent.load_weights(savedir) + + def save_weights(self, savedir: str) -> None: + self.leader_agent.save_weights(savedir) + self.follower_agent.save_weights(savedir) diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/envs/__init__.py b/third_party/AnyBimanual/third_party/YARR/yarr/envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/envs/env.py b/third_party/AnyBimanual/third_party/YARR/yarr/envs/env.py new file mode 100644 index 0000000000000000000000000000000000000000..5fd27f977dd1925bd9e581648e4966f9b78af0f9 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/envs/env.py @@ -0,0 +1,64 @@ +from abc import ABC, abstractmethod +from typing import Any, List + +import numpy as np + +from yarr.utils.observation_type import ObservationElement +from yarr.utils.transition import Transition + + +class Env(ABC): + + def __init__(self): + self._active_task_id = 0 + self._eval_env = False + + @property + def eval(self): + return self._eval_env + + @eval.setter + def eval(self, eval): + self._eval_env = eval + + @property + def active_task_id(self) -> int: + return self._active_task_id + + @abstractmethod + def launch(self) -> None: + pass + + def shutdown(self) -> None: + pass + + @abstractmethod + def reset(self) -> dict: + pass + + @abstractmethod + def step(self, action: np.ndarray) -> Transition: + pass + + @property + @abstractmethod + def observation_elements(self) -> List[ObservationElement]: + pass + + @property + @abstractmethod + def action_shape(self) -> tuple: + pass + + @property + @abstractmethod + def env(self) -> Any: + pass + + +class MultiTaskEnv(Env): + + @property + @abstractmethod + def num_tasks(self) -> int: + pass \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/envs/rlbench_env.py b/third_party/AnyBimanual/third_party/YARR/yarr/envs/rlbench_env.py new file mode 100644 index 0000000000000000000000000000000000000000..9562105fb8c1b3f4dd38a982ddf1203d1bfd2db1 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/envs/rlbench_env.py @@ -0,0 +1,332 @@ +import re +from typing import Type, List + +import numpy as np +try: + from rlbench import ObservationConfig, Environment, CameraConfig +except (ModuleNotFoundError, ImportError) as e: + print("You need to install RLBench: 'https://github.com/stepjam/RLBench'") + raise e +from rlbench.action_modes.action_mode import ActionMode +from rlbench.backend.observation import BimanualObservation, Observation +from rlbench.backend.task import Task +from rlbench.backend.task import BimanualTask + +from helpers.clip.core.clip import tokenize + +from yarr.envs.env import Env, MultiTaskEnv +from yarr.utils.observation_type import ObservationElement +from yarr.utils.transition import Transition +from yarr.utils.process_str import change_case + +import logging + + +ROBOT_STATE_KEYS = ['joint_velocities', 'joint_positions', 'joint_forces', + 'gripper_open', 'gripper_pose', + 'gripper_joint_positions', 'gripper_touch_forces', + 'task_low_dim_state', 'misc', 'left', 'right'] + + +# ..todo:: possibly duplicated code. +def _extract_obs_bimanual(obs: BimanualObservation, channels_last: bool, observation_config: ObservationConfig): + obs_dict = vars(obs) + obs_dict = {k: v for k, v in obs_dict.items() if v is not None} + + right_robot_state = obs.get_low_dim_data(obs.right) + left_robot_state = obs.get_low_dim_data(obs.left) + + obs_dict = {k: v for k, v in obs_dict.items() + if k not in ROBOT_STATE_KEYS} + + if not channels_last: + # Swap channels from last dim to 1st dim + obs_dict = {k: np.transpose(v, [2, 0, 1]) if v.ndim == 3 else np.expand_dims(v, 0) + for k, v in obs.perception_data.items() if v is not None} + else: + # Add extra dim to depth data + obs_dict = {k: v if v.ndim == 3 else np.expand_dims(v, -1) + for k, v in obs.perception_data.items() if v is not None} + + if observation_config.robot_name == 'right': + obs_dict['low_dim_state'] = right_robot_state.astype(np.float32) + obs_dict['ignore_collisions'] = np.array([obs.right.ignore_collisions], dtype=np.float32) + elif observation_config.robot_name == 'left': + obs_dict['low_dim_state'] = left_robot_state.astype(np.float32) + obs_dict['ignore_collisions'] = np.array([obs.left.ignore_collisions], dtype=np.float32) + else: + obs_dict['right_low_dim_state'] = right_robot_state.astype(np.float32) + obs_dict['left_low_dim_state'] = left_robot_state.astype(np.float32) + obs_dict['right_ignore_collisions'] = np.array([obs.right.ignore_collisions], dtype=np.float32) + obs_dict['left_ignore_collisions'] = np.array([obs.left.ignore_collisions], dtype=np.float32) + + for (k, v) in [(k, v) for k, v in obs_dict.items() if 'point_cloud' in k]: + # ..TODO:: + obs_dict[k] = v.astype(np.float16) + + for camera_name, config in observation_config.camera_configs.items(): + if config.point_cloud: + obs_dict[f'{camera_name}_camera_extrinsics'] = obs.misc[f'{camera_name}_camera_extrinsics'] + obs_dict[f'{camera_name}_camera_intrinsics'] = obs.misc[f'{camera_name}_camera_intrinsics'] + return obs_dict + + +def _extract_obs_unimanual(obs: Observation, channels_last: bool, observation_config): + obs_dict = vars(obs) + obs_dict = {k: v for k, v in obs_dict.items() if v is not None} + robot_state = obs.get_low_dim_data() + # Remove all of the individual state elements + obs_dict = {k: v for k, v in obs_dict.items() + if k not in ROBOT_STATE_KEYS} + if not channels_last: + # Swap channels from last dim to 1st dim + obs_dict = {k: np.transpose( + v, [2, 0, 1]) if v.ndim == 3 else np.expand_dims(v, 0) + for k, v in obs_dict.items()} + else: + # Add extra dim to depth data + obs_dict = {k: v if v.ndim == 3 else np.expand_dims(v, -1) + for k, v in obs_dict.items()} + obs_dict['low_dim_state'] = np.array(robot_state, dtype=np.float32) + obs_dict['ignore_collisions'] = np.array([obs.ignore_collisions], dtype=np.float32) + for (k, v) in [(k, v) for k, v in obs_dict.items() if 'point_cloud' in k]: + obs_dict[k] = v.astype(np.float32) + + for config, name in [ + (observation_config.left_shoulder_camera, 'left_shoulder'), + (observation_config.right_shoulder_camera, 'right_shoulder'), + (observation_config.front_camera, 'front'), + (observation_config.wrist_camera, 'wrist'), + (observation_config.overhead_camera, 'overhead')]: + if config.point_cloud: + obs_dict['%s_camera_extrinsics' % name] = obs.misc['%s_camera_extrinsics' % name] + obs_dict['%s_camera_intrinsics' % name] = obs.misc['%s_camera_intrinsics' % name] + return obs_dict + + +def _get_cam_observation_elements(camera: CameraConfig, prefix: str, channels_last): + elements = [] + img_s = list(camera.image_size) + shape = img_s + [3] if channels_last else [3] + img_s + if camera.rgb: + elements.append( + ObservationElement('%s_rgb' % prefix, shape, np.uint8)) + if camera.point_cloud: + elements.append( + ObservationElement('%s_point_cloud' % prefix, shape, np.float32)) + elements.append( + ObservationElement('%s_camera_extrinsics' % prefix, (4, 4), + np.float32)) + elements.append( + ObservationElement('%s_camera_intrinsics' % prefix, (3, 3), + np.float32)) + if camera.depth: + shape = img_s + [1] if channels_last else [1] + img_s + elements.append( + ObservationElement('%s_depth' % prefix, shape, np.float32)) + if camera.mask: + raise NotImplementedError() + + return elements + + +def _observation_elements(observation_config, channels_last) -> List[ObservationElement]: + elements = [] + robot_state_len = 0 + if observation_config.joint_velocities: + robot_state_len += 7 + if observation_config.joint_positions: + robot_state_len += 7 + if observation_config.joint_forces: + robot_state_len += 7 + if observation_config.gripper_open: + robot_state_len += 1 + if observation_config.gripper_pose: + robot_state_len += 7 + if observation_config.gripper_joint_positions: + robot_state_len += 2 + if observation_config.gripper_touch_forces: + robot_state_len += 2 + if observation_config.task_low_dim_state: + raise NotImplementedError() + if robot_state_len > 0: + if observation_config.robot_name == 'bimanual': + elements.append(ObservationElement( + 'right_low_dim_state', (robot_state_len,), np.float32)) + elements.append(ObservationElement( + 'left_low_dim_state', (robot_state_len,), np.float32)) + elif observation_config.robot_name in ['unimanual', 'left', 'right']: + elements.append(ObservationElement('low_dim_state', (robot_state_len,), np.float32)) + elements.extend(_get_cam_observation_elements( + observation_config.left_shoulder_camera, 'left_shoulder', channels_last)) + elements.extend(_get_cam_observation_elements( + observation_config.right_shoulder_camera, 'right_shoulder', channels_last)) + elements.extend(_get_cam_observation_elements( + observation_config.front_camera, 'front', channels_last)) + elements.extend(_get_cam_observation_elements( + observation_config.wrist_camera, 'wrist', channels_last)) + return elements + + +class RLBenchEnv(Env): + + def __init__(self, task_class: Type[Task], + observation_config: ObservationConfig, + action_mode: ActionMode, + dataset_root: str = '', + channels_last=False, + headless=True, + include_lang_goal_in_obs=False): + super(RLBenchEnv, self).__init__() + self._task_class = task_class + self._observation_config = observation_config + self._channels_last = channels_last + self._include_lang_goal_in_obs = include_lang_goal_in_obs + if issubclass(task_class, BimanualTask): + robot_setup = "dual_panda" + else: + robot_setup = "panda" + self._rlbench_env = Environment( + action_mode=action_mode, obs_config=observation_config, + dataset_root=dataset_root, headless=headless, robot_setup=robot_setup) + self._task = None + self._lang_goal = 'unknown goal' + + + def extract_obs(self, obs: Observation): + if isinstance(obs, BimanualObservation): + extracted_obs = _extract_obs_bimanual(obs, self._channels_last, self._observation_config) + else: + extracted_obs = _extract_obs_unimanual(obs, self._channels_last, self._observation_config) + if self._include_lang_goal_in_obs: + extracted_obs['lang_goal_tokens'] = tokenize([self._lang_goal])[0].numpy() + return extracted_obs + + def launch(self): + self._rlbench_env.launch() + self._task = self._rlbench_env.get_task(self._task_class) + + def shutdown(self): + self._rlbench_env.shutdown() + + def reset(self) -> dict: + descriptions, obs = self._task.reset() + self._lang_goal = descriptions[0] # first description variant + extracted_obs = self.extract_obs(obs) + return extracted_obs + + def step(self, action: np.ndarray) -> Transition: + obs, reward, terminal = self._task.step(action) + obs = self.extract_obs(obs) + return Transition(obs, reward, terminal) + + @property + def observation_elements(self) -> List[ObservationElement]: + return _observation_elements(self._observation_config, self._channels_last) + + @property + def action_shape(self): + return (self._rlbench_env.action_size, ) + + @property + def env(self) -> Environment: + return self._rlbench_env + + +class MultiTaskRLBenchEnv(MultiTaskEnv): + + def __init__(self, + task_classes: List[Type[Task]], + observation_config: ObservationConfig, + action_mode: ActionMode, + dataset_root: str = '', + channels_last=False, + headless=True, + swap_task_every: int = 1, + include_lang_goal_in_obs=False): + super(MultiTaskRLBenchEnv, self).__init__() + self._task_classes = task_classes + self._observation_config = observation_config + self._channels_last = channels_last + self._include_lang_goal_in_obs = include_lang_goal_in_obs + if issubclass(task_classes[0], BimanualTask): + robot_setup = "dual_panda" + else: + robot_setup = "panda" + self._rlbench_env = Environment( + action_mode=action_mode, obs_config=observation_config, + dataset_root=dataset_root, headless=headless, robot_setup=robot_setup) + self._task = None + self._task_name = '' + self._lang_goal = 'unknown goal' + self._swap_task_every = swap_task_every + self._rlbench_env + self._episodes_this_task = 0 + self._active_task_id = -1 + + self._task_name_to_idx = {change_case(tc.__name__):i for i, tc in enumerate(self._task_classes)} + + def _set_new_task(self, shuffle=False): + if shuffle: + self._active_task_id = np.random.randint(0, len(self._task_classes)) + else: + self._active_task_id = (self._active_task_id + 1) % len(self._task_classes) + task = self._task_classes[self._active_task_id] + self._task = self._rlbench_env.get_task(task) + + def set_task(self, task_name: str): + self._active_task_id = self._task_name_to_idx[task_name] + task = self._task_classes[self._active_task_id] + self._task = self._rlbench_env.get_task(task) + + descriptions, _ = self._task.reset() + self._lang_goal = descriptions[0] # first description variant + + def extract_obs(self, obs: Observation): + if obs.is_bimanual: + extracted_obs = _extract_obs_bimanual(obs, self._channels_last, self._observation_config) + else: + extracted_obs = _extract_obs_unimanual(obs, self._channels_last, self._observation_config) + if self._include_lang_goal_in_obs: + extracted_obs['lang_goal_tokens'] = tokenize([self._lang_goal])[0].numpy() + return extracted_obs + + def launch(self): + self._rlbench_env.launch() + self._set_new_task() + + def shutdown(self): + self._rlbench_env.shutdown() + + def reset(self) -> dict: + if self._episodes_this_task == self._swap_task_every: + self._set_new_task() + self._episodes_this_task = 0 + self._episodes_this_task += 1 + + descriptions, obs = self._task.reset() + self._lang_goal = descriptions[0] # first description variant + extracted_obs = self.extract_obs(obs) + + return extracted_obs + + def step(self, action: np.ndarray) -> Transition: + obs, reward, terminal = self._task.step(action) + obs = self.extract_obs(obs) + return Transition(obs, reward, terminal) + + @property + def observation_elements(self) -> List[ObservationElement]: + return _observation_elements(self._observation_config, self._channels_last) + + @property + def action_shape(self): + return (self._rlbench_env.action_size, ) + + @property + def env(self) -> Environment: + return self._rlbench_env + + @property + def num_tasks(self) -> int: + return len(self._task_classes) diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/__init__.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/prioritized_replay_buffer.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/prioritized_replay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..706706a601d1d0bb6d2d6273185d60d33d5dd415 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/prioritized_replay_buffer.py @@ -0,0 +1,217 @@ +"""An implementation of Prioritized Experience Replay (PER). + +This implementation is based on the paper "Prioritized Experience Replay" +by Tom Schaul et al. (2015). +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from .uniform_replay_buffer import * +from .sum_tree import * +import numpy as np + + +PRIORITY = 'priority' + + +class PrioritizedReplayBuffer(UniformReplayBuffer): + """An out-of-graph Replay Buffer for Prioritized Experience Replay. + + See uniform_replay_buffer.py for details. + """ + + def __init__(self, *args, **kwargs): + """Initializes OutOfGraphPrioritizedReplayBuffer.""" + super(PrioritizedReplayBuffer, self).__init__(*args, **kwargs) + self._sum_tree = SumTree(self._replay_capacity) + + def get_storage_signature(self) -> Tuple[List[ReplayElement], + List[ReplayElement]]: + """Returns a default list of elements to be stored in this replay memory. + + Note - Derived classes may return a different signature. + + Returns: + dict of ReplayElements defining the type of the contents stored. + """ + storage_elements, obs_elements = super( + PrioritizedReplayBuffer, self).get_storage_signature() + storage_elements.append(ReplayElement(PRIORITY, (), np.float32),) + + return storage_elements, obs_elements + + def add(self, action, reward, terminal, timeout, priority=None, **kwargs): + kwargs['priority'] = priority + super(PrioritizedReplayBuffer, self).add( + action, reward, terminal, timeout, **kwargs) + + def _add(self, kwargs: dict): + """Internal add method to add to the storage arrays. + + Args: + kwargs: All the elements in a transition. + """ + with self._lock: + cursor = self.cursor() + priority = kwargs[PRIORITY] + if priority is None: + priority = self._sum_tree.max_recorded_priority + + if self._disk_saving: + term = self._store[TERMINAL] + term[cursor] = kwargs[TERMINAL] + self._store[TERMINAL] = term + + with open(join(self._save_dir, '%d.replay' % cursor), 'wb') as f: + pickle.dump(kwargs, f) + # If first add, then pad for correct wrapping + if self._add_count.value == 0: + self._add_initial_to_disk(kwargs) + else: + for name, data in kwargs.items(): + item = self._store[name] + item[cursor] = data + self._store[name] = item + + + self._sum_tree.set(self.cursor(), priority) + self._add_count.value += 1 + self.invalid_range = invalid_range( + self.cursor(), self._replay_capacity, self._timesteps, + self._update_horizon) + + def add_final(self, **kwargs): + """Adds a transition to the replay memory. + Args: + **kwargs: The remaining args + """ + # if self.is_empty() or self._store['terminal'][self.cursor() - 1] != 1: + # raise ValueError('The previous transition was not terminal.') + self._check_add_types(kwargs, self._obs_signature) + transition = self._final_transition(kwargs) + for element_type in self._storage_signature: + # 0 priority for final observation. + if element_type.name == PRIORITY: + transition[element_type.name] = 0.0 + self._add(transition) + + def sample_index_batch(self, batch_size): + """Returns a batch of valid indices sampled as in Schaul et al. (2015). + + Args: + batch_size: int, number of indices returned. + + Returns: + list of ints, a batch of valid indices sampled uniformly. + + Raises: + Exception: If the batch was not constructed after maximum number of tries. + """ + # Sample stratified indices. Some of them might be invalid. + indices = self._sum_tree.stratified_sample(batch_size) + allowed_attempts = self._max_sample_attempts + for i in range(len(indices)): + if not self.is_valid_transition(indices[i]): + if allowed_attempts == 0: + raise RuntimeError( + 'Max sample attempts: Tried {} times but only sampled {}' + ' valid indices. Batch size is {}'. + format(self._max_sample_attempts, i, batch_size)) + index = indices[i] + while not self.is_valid_transition( + index) and allowed_attempts > 0: + # If index i is not valid keep sampling others. Note that this + # is not stratified. + index = self._sum_tree.sample() + allowed_attempts -= 1 + indices[i] = index + return indices + + def sample_transition_batch(self, batch_size=None, indices=None, + pack_in_dict=True): + """Returns a batch of transitions with extra storage and the priorities. + + The extra storage are defined through the extra_storage_types constructor + argument. + + When the transition is terminal next_state_batch has undefined contents. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + indices: None or list of ints, the indices of every transition in the + batch. If None, sample the indices uniformly. + + Returns: + transition_batch: tuple of np.arrays with the shape and type as in + get_transition_elements(). + """ + transition = super( + PrioritizedReplayBuffer, self).sample_transition_batch( + batch_size, indices, pack_in_dict=False) + + transition_elements = self.get_transition_elements(batch_size) + transition_names = [e.name for e in transition_elements] + probabilities_index = transition_names.index('sampling_probabilities') + indices_index = transition_names.index('indices') + indices = transition[indices_index] + # The parent returned an empty array for the probabilities. Fill it with the + # contents of the sum tree. + transition[probabilities_index][:] = self.get_priority(indices) + batch_arrays = transition + if pack_in_dict: + batch_arrays = self.unpack_transition(transition, + transition_elements) + return batch_arrays + + def set_priority(self, indices, priorities): + """Sets the priority of the given elements according to Schaul et al. + + Args: + indices: np.array with dtype int32, of indices in range + [0, replay_capacity). + priorities: float, the corresponding priorities. + """ + assert indices.dtype == np.int32, ('Indices must be integers, ' + 'given: {}'.format(indices.dtype)) + for index, priority in zip(indices, priorities): + self._sum_tree.set(index, priority) + + def get_priority(self, indices): + """Fetches the priorities correspond to a batch of memory indices. + + For any memory location not yet used, the corresponding priority is 0. + + Args: + indices: np.array with dtype int32, of indices in range + [0, replay_capacity). + + Returns: + priorities: float, the corresponding priorities. + """ + assert indices.shape, 'Indices must be an array.' + assert indices.dtype == np.int32, ('Indices must be int32s, ' + 'given: {}'.format(indices.dtype)) + batch_size = len(indices) + priority_batch = np.empty((batch_size), dtype=np.float32) + for i, memory_index in enumerate(indices): + priority_batch[i] = self._sum_tree.get(memory_index) + return priority_batch + + def get_transition_elements(self, batch_size=None): + """Returns a 'type signature' for sample_transition_batch. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + Returns: + signature: A namedtuple describing the method's return type signature. + """ + parent_transition_type = ( + super(PrioritizedReplayBuffer, + self).get_transition_elements(batch_size)) + probablilities_type = [ + ReplayElement('sampling_probabilities', (batch_size,), np.float32) + ] + return parent_transition_type + probablilities_type diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/replay_buffer.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/replay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..e01d4a8d707f84cd168f66cc28b3a6f1b9c972d3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/replay_buffer.py @@ -0,0 +1,71 @@ +from abc import ABC +from typing import Tuple, List + +class ReplayElement(object): + def __init__(self, name, shape, type, is_observation=False): + self.name = name + self.shape = shape + self.type = type + self.is_observation = is_observation + + +class ReplayBuffer(ABC): + + def replay_capacity(self): + pass + + def batch_size(self): + pass + + def get_storage_signature(self) -> Tuple[List[ReplayElement], + List[ReplayElement]]: + pass + + def add(self, action, reward, terminal, timeout, **kwargs): + pass + + def add_final(self, **kwargs): + pass + + def is_empty(self): + pass + + def is_full(self): + pass + + def cursor(self): + pass + + def set_cursor(self): + pass + + def get_range(self, array, start_index, end_index): + pass + + def get_range_stack(self, array, start_index, end_index, terminals=None): + pass + + def get_terminal_stack(self, index): + pass + + def is_valid_transition(self, index): + pass + + def sample_index_batch(self, batch_size): + pass + + def unpack_transition(self, transition_tensors, transition_type): + pass + + def sample_transition_batch(self, batch_size=None, indices=None, + pack_in_dict=True): + pass + + def get_transition_elements(self, batch_size=None): + pass + + def shutdown(self): + pass + + def using_disk(self): + pass diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/sum_tree.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/sum_tree.py new file mode 100644 index 0000000000000000000000000000000000000000..c6ff274a8652426a5eb199b4c09ef38df0d40e9a --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/sum_tree.py @@ -0,0 +1,201 @@ +"""A sum tree data structure. + +Used for prioritized experience replay. See prioritized_replay_buffer.py +and Schaul et al. (2015). +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import random + +import numpy as np + + +class SumTree(object): + """A sum tree data structure for storing replay priorities. + + A sum tree is a complete binary tree whose leaves contain values called + priorities. Internal nodes maintain the sum of the priorities of all leaf + nodes in their subtree. + + For capacity = 4, the tree may look like this: + + +---+ + |2.5| + +-+-+ + | + +-------+--------+ + | | + +-+-+ +-+-+ + |1.5| |1.0| + +-+-+ +-+-+ + | | + +----+----+ +----+----+ + | | | | + +-+-+ +-+-+ +-+-+ +-+-+ + |0.5| |1.0| |0.5| |0.5| + +---+ +---+ +---+ +---+ + + This is stored in a list of numpy arrays: + self.nodes = [ [2.5], [1.5, 1], [0.5, 1, 0.5, 0.5] ] + + For conciseness, we allocate arrays as powers of two, and pad the excess + elements with zero values. + + This is similar to the usual array-based representation of a complete binary + tree, but is a little more user-friendly. + """ + + def __init__(self, capacity, nodes=None): + """Creates the sum tree data structure for the given replay capacity. + + Args: + capacity: int, the maximum number of elements that can be stored in this + data structure. + nodes: storage list for storing nodes + + Raises: + ValueError: If requested capacity is not positive. + """ + assert isinstance(capacity, int) + if capacity <= 0: + raise ValueError('Sum tree capacity should be positive. Got: {}'. + format(capacity)) + + self.nodes = [] if nodes is None else nodes + tree_depth = int(math.ceil(np.log2(capacity))) + level_size = 1 + for _ in range(tree_depth + 1): + nodes_at_this_depth = np.zeros(level_size) + self.nodes.append(nodes_at_this_depth) + + level_size *= 2 + + self.max_recorded_priority = 1.0 + + def _total_priority(self): + """Returns the sum of all priorities stored in this sum tree. + + Returns: + float, sum of priorities stored in this sum tree. + """ + return self.nodes[0][0] + + def sample(self, query_value=None): + """Samples an element from the sum tree. + + Each element has probability p_i / sum_j p_j of being picked, where p_i is + the (positive) value associated with node i (possibly unnormalized). + + Args: + query_value: float in [0, 1], used as the random value to select a + sample. If None, will select one randomly in [0, 1). + + Returns: + int, a random element from the sum tree. + + Raises: + Exception: If the sum tree is empty (i.e. its node values sum to 0), or if + the supplied query_value is larger than the total sum. + """ + if self._total_priority() == 0.0: + raise Exception('Cannot sample from an empty sum tree.') + + if query_value and (query_value < 0. or query_value > 1.): + raise ValueError('query_value must be in [0, 1].') + + # Sample a value in range [0, R), where R is the value stored at the root. + query_value = random.random() if query_value is None else query_value + query_value *= self._total_priority() + + # Now traverse the sum tree. + node_index = 0 + for nodes_at_this_depth in self.nodes[1:]: + # Compute children of previous depth's node. + left_child = node_index * 2 + + left_sum = nodes_at_this_depth[left_child] + # Each subtree describes a range [0, a), where a is its value. + if query_value < left_sum: # Recurse into left subtree. + node_index = left_child + else: # Recurse into right subtree. + node_index = left_child + 1 + # Adjust query to be relative to right subtree. + query_value -= left_sum + + return node_index + + def stratified_sample(self, batch_size): + """Performs stratified sampling using the sum tree. + + Let R be the value at the root (total value of sum tree). This method will + divide [0, R) into batch_size segments, pick a random number from each of + those segments, and use that random number to sample from the sum_tree. This + is as specified in Schaul et al. (2015). + + Args: + batch_size: int, the number of strata to use. + Returns: + list of batch_size elements sampled from the sum tree. + + Raises: + Exception: If the sum tree is empty (i.e. its node values sum to 0). + """ + if self._total_priority() == 0.0: + raise Exception('Cannot sample from an empty sum tree.') + + bounds = np.linspace(0., 1., batch_size + 1) + assert len(bounds) == batch_size + 1 + segments = [(bounds[i], bounds[i + 1]) for i in range(batch_size)] + # TODO removed for now + # query_values = [random.uniform(x[0], x[1]) for x in segments] + query_values = [random.uniform(0, 1) for x in segments] + return [self.sample(query_value=x) for x in query_values] + + def get(self, node_index): + """Returns the value of the leaf node corresponding to the index. + + Args: + node_index: The index of the leaf node. + Returns: + The value of the leaf node. + """ + return self.nodes[-1][node_index] + + def set(self, node_index, value): + """Sets the value of a leaf node and updates internal nodes accordingly. + + This operation takes O(log(capacity)). + Args: + node_index: int, the index of the leaf node to be updated. + value: float, the value which we assign to the node. This value must be + nonnegative. Setting value = 0 will cause the element to never be + sampled. + + Raises: + ValueError: If the given value is negative. + """ + if value < 0.0: + raise ValueError('Sum tree values should be nonnegative. Got {}'. + format(value)) + self.max_recorded_priority = max(value, self.max_recorded_priority) + + delta_value = value - self.nodes[-1][node_index] + + # # Now traverse back the tree, adjusting all sums along the way. + # for nodes_at_this_depth in reversed(self.nodes): + # # Note: Adding a delta leads to some tolerable numerical inaccuracies. + # nodes_at_this_depth[node_index] += delta_value + # self.nodes[] + # node_index //= 2 + + for nodes_at_this_depth_idx in reversed(range(len(self.nodes))): + nodes_at_this_depth = self.nodes[nodes_at_this_depth_idx] + nodes_at_this_depth[node_index] += delta_value + self.nodes[nodes_at_this_depth_idx] = nodes_at_this_depth + node_index //= 2 + + assert node_index == 0, ('Sum tree traversal failed, final node index ' + 'is not 0.') diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/task_uniform_replay_buffer.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/task_uniform_replay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..7c343d1b2fdae8b5c19f7ec43c525c9bdff62137 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/task_uniform_replay_buffer.py @@ -0,0 +1,220 @@ +import numpy as np +import os +from os.path import join +import pickle +import math +from yarr.replay_buffer.uniform_replay_buffer import UniformReplayBuffer +from yarr.replay_buffer.uniform_replay_buffer import invalid_range + +from yarr.replay_buffer.replay_buffer import ReplayBuffer, ReplayElement +from yarr.utils.observation_type import ObservationElement + +ACTION = 'action' +REWARD = 'reward' +TERMINAL = 'terminal' +TIMEOUT = 'timeout' +INDICES = 'indices' +TASK = 'task' + + +class TaskUniformReplayBuffer(UniformReplayBuffer): + """ + A uniform with uniform task sampling for each batch + """ + + def __init__(self, *args, **kwargs): + """Initializes OutOfGraphPrioritizedReplayBuffer.""" + super(TaskUniformReplayBuffer, self).__init__(*args, **kwargs) + self._task_idxs = dict() + + def _add(self, kwargs: dict): + """Internal add method to add to the storage arrays. + + Args: + kwargs: All the elements in a transition. + """ + with self._lock: + cursor = self.cursor() + + if self._disk_saving: + term = self._store[TERMINAL] + term[cursor] = kwargs[TERMINAL] + self._store[TERMINAL] = term + + + ## reduce size + for k, v in kwargs.items(): + try: + if 'float' in v.dtype.name and v.size > 100: + v = v.astype(np.float16) + kwargs[k] = v + except: + pass + + + with open(join(self._save_dir, '%d.replay' % cursor), 'wb') as f: + pickle.dump(kwargs, f) + # If first add, then pad for correct wrapping + if self._add_count.value == 0: + self._add_initial_to_disk(kwargs) + else: + for name, data in kwargs.items(): + item = self._store[name] + item[cursor] = data + self._store[name] = item + with self._add_count.get_lock(): + task = kwargs[TASK] + if task not in self._task_idxs: + self._task_idxs[task] = [cursor] + else: + self._task_idxs[task] = self._task_idxs[task] + [cursor] + self._add_count.value += 1 + + self.invalid_range = invalid_range( + self.cursor(), self._replay_capacity, self._timesteps, + self._update_horizon) + def load_add(self, kwargs: dict): + """Method to add data to buffer without saving to disk. + + Args: + kwargs: All the elements in a transition. + """ + with self._lock: + cursor = self.cursor() + term = self._store[TERMINAL] + term[cursor] = kwargs[TERMINAL] + self._store[TERMINAL] = term + + + ## reduce size + for k, v in kwargs.items(): + # print(k) + try: + if 'float' in v.dtype.name and v.size > 100: + v = v.astype(np.float16) + kwargs[k] = v + # print(k) + except: + pass + + # If first add, then pad for correct wrapping + if self._add_count.value == 0: + self._add_initial_to_disk(kwargs) + with self._add_count.get_lock(): + task = kwargs[TASK] + if task not in self._task_idxs: + self._task_idxs[task] = [cursor] + else: + self._task_idxs[task] = self._task_idxs[task] + [cursor] + self._add_count.value += 1 + + self.invalid_range = invalid_range( + self.cursor(), self._replay_capacity, self._timesteps, + self._update_horizon) + + def sample_index_batch(self, + batch_size): + """Returns a batch of valid indices sampled uniformly. + + Args: + batch_size: int, number of indices returned. + + Returns: + list of ints, a batch of valid indices sampled uniformly across tasks. + + Raises: + RuntimeError: If the batch was not constructed after maximum number of + tries. + """ + if self.is_full(): + min_id = (self.cursor() - self._replay_capacity + + self._timesteps - 1) + max_id = self.cursor() - self._update_horizon + else: + min_id = 0 + max_id = self.cursor() - self._update_horizon + if max_id <= min_id: + raise RuntimeError( + 'Cannot sample a batch with fewer than stack size ' + '({}) + update_horizon ({}) transitions.'. + format(self._timesteps, self._update_horizon)) + + tasks = list(self._task_idxs.keys()) + attempt_count = 0 + found_indicies = False + + # uniform distribution of tasks + while not found_indicies and attempt_count < 1000: + # sample random tasks of batch_size length + sampled_tasks = list(np.random.choice(tasks, batch_size, replace=(batch_size > len(tasks)))) + potential_indices = [] + for task in sampled_tasks: + # DDP setting where each GPU only sees a fraction of the data + # reference: https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py + task_data_size = len(self._task_idxs[task]) + num_samples = math.ceil(task_data_size / self._num_replicas) + total_size = num_samples * self._num_replicas + task_indices = self._task_idxs[task][self._rank:total_size:self._num_replicas] + + sampled_task_idx = np.random.choice(task_indices, 1)[0] + per_task_attempt_count = 0 + + # Argh.. this is slow + while not self.is_valid_transition(sampled_task_idx) and \ + per_task_attempt_count < self._max_sample_attempts: + sampled_task_idx = np.random.choice(task_indices, 1)[0] + per_task_attempt_count += 1 + + if not self.is_valid_transition(sampled_task_idx): + attempt_count += 1 + continue + else: + potential_indices.append(sampled_task_idx) + found_indicies = len(potential_indices) == batch_size + indices = potential_indices + + if len(indices) != batch_size: + raise RuntimeError( + 'Max sample attempts: Tried {} times but only sampled {}' + ' valid indices. Batch size is {}'. + format(self._max_sample_attempts, len(indices), batch_size)) + + return indices + + def get_transition_elements(self, batch_size=None): + """Returns a 'type signature' for sample_transition_batch. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + Returns: + signature: A namedtuple describing the method's return type signature. + """ + batch_size = self._batch_size if batch_size is None else batch_size + + transition_elements = [ + ReplayElement(ACTION, (batch_size, self._timesteps) + self._action_shape, + self._action_dtype), + ReplayElement(REWARD, (batch_size, self._timesteps) + self._reward_shape, + self._reward_dtype), + ReplayElement(TERMINAL, (batch_size, self._timesteps), np.int8), + ReplayElement(TIMEOUT, (batch_size, self._timesteps), bool), + ReplayElement(INDICES, (batch_size, self._timesteps), np.int32), + ] + + for element in self._observation_elements: + transition_elements.append(ReplayElement( + element.name, + (batch_size, self._timesteps) + tuple(element.shape), + element.type, True)) + transition_elements.append(ReplayElement( + element.name + '_tp1', + (batch_size, self._timesteps) + tuple(element.shape), + element.type, True)) + + for element in self._extra_replay_elements: + transition_elements.append(ReplayElement( + element.name, + (batch_size,) + tuple(element.shape), + element.type)) + return transition_elements diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/uniform_replay_buffer.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/uniform_replay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..664bea8b5b67368fa5239285db3fb064ea473222 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/uniform_replay_buffer.py @@ -0,0 +1,804 @@ +"""The standard DQN replay memory. + +This implementation is an out-of-graph replay memory + in-graph wrapper. It +supports vanilla n-step updates of the form typically found in the literature, +i.e. where rewards are accumulated for n steps and the intermediate trajectory +is not exposed to the agent. This does not allow, for example, performing +off-policy corrections. +""" +import ctypes +import collections +import concurrent.futures +import os +from os.path import join +import pickle +from typing import List, Tuple, Type +import time +import math +# from threading import Lock +import multiprocessing as mp +from multiprocessing import Lock +import numpy as np +import logging + +from natsort import natsort + +from yarr.replay_buffer.replay_buffer import ReplayBuffer, ReplayElement +from yarr.utils.observation_type import ObservationElement + +import torch.distributed as dist + +# Defines a type describing part of the tuple returned by the replay +# memory. Each element of the tuple is a tensor of shape [batch, ...] where +# ... is defined the 'shape' field of ReplayElement. The tensor type is +# given by the 'type' field. The 'name' field is for convenience and ease of +# debugging. + + +# String constants for storage +ACTION = 'action' +REWARD = 'reward' +TERMINAL = 'terminal' +TIMEOUT = 'timeout' +INDICES = 'indices' + + +def invalid_range(cursor, replay_capacity, stack_size, update_horizon): + """Returns a array with the indices of cursor-related invalid transitions. + + There are update_horizon + stack_size invalid indices: + - The update_horizon indices before the cursor, because we do not have a + valid N-step transition (including the next state). + - The stack_size indices on or immediately after the cursor. + If N = update_horizon, K = stack_size, and the cursor is at c, invalid + indices are: + c - N, c - N + 1, ..., c, c + 1, ..., c + K - 1. + + It handles special cases in a circular buffer in the beginning and the end. + + Args: + cursor: int, the position of the cursor. + replay_capacity: int, the size of the replay memory. + stack_size: int, the size of the stacks returned by the replay memory. + update_horizon: int, the agent's update horizon. + Returns: + np.array of size stack_size with the invalid indices. + """ + assert cursor < replay_capacity + return np.array( + [(cursor - update_horizon + i) % replay_capacity + for i in range(stack_size + update_horizon)]) + + +class UniformReplayBuffer(ReplayBuffer): + """A simple out-of-graph Replay Buffer. + + Stores transitions, state, action, reward, next_state, terminal (and any + extra contents specified) in a circular buffer and provides a uniform + transition sampling function. + + When the states consist of stacks of observations storing the states is + inefficient. This class writes observations and constructs the stacked states + at sample time. + + Attributes: + _add_count: int, counter of how many transitions have been added (including + the blank ones at the beginning of an episode). + invalid_range: np.array, an array with the indices of cursor-related invalid + transitions + """ + + def __init__(self, + batch_size: int = 32, + timesteps: int = 1, + replay_capacity: int = int(1e6), + update_horizon: int = 1, + gamma: float = 0.99, + max_sample_attempts: int = 10000, + action_shape: tuple = (), + action_dtype: Type[np.dtype] = np.float32, + reward_shape: tuple = (), + reward_dtype: Type[np.dtype] = np.float32, + observation_elements: List[ObservationElement] = None, + extra_replay_elements: List[ReplayElement] = None, + save_dir: str = None, + purge_replay_on_shutdown: bool = True, + num_replicas: int = None, + rank: int = None, + ): + """Initializes OutOfGraphReplayBuffer. + + Args: + batch_size: int. + timesteps: int, number of frames to use in state stack. + replay_capacity: int, number of transitions to keep in memory. + update_horizon: int, length of update ('n' in n-step update). + gamma: int, the discount factor. + max_sample_attempts: int, the maximum number of attempts allowed to + get a sample. + action_shape: tuple of ints, the shape for the action vector. + Empty tuple means the action is a scalar. + action_dtype: np.dtype, type of elements in the action. + reward_shape: tuple of ints, the shape of the reward vector. + Empty tuple means the reward is a scalar. + reward_dtype: np.dtype, type of elements in the reward. + observation_elements: list of ObservationElement defining the type of + the extra contents that will be stored and returned. + extra_storage_elements: list of ReplayElement defining the type of + the extra contents that will be stored and returned. + + Raises: + ValueError: If replay_capacity is too small to hold at least one + transition. + """ + if num_replicas is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + self._num_replicas = dist.get_world_size() + if rank is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + self._rank = dist.get_rank() + if self._rank >= self._num_replicas or self._rank < 0: + raise ValueError( + "Invalid rank {}, rank should be in the interval" + " [0, {}]".format(self._rank, self._num_replicas - 1)) + + if observation_elements is None: + observation_elements = [] + if extra_replay_elements is None: + extra_replay_elements = [] + + if replay_capacity < update_horizon + timesteps: + raise ValueError('There is not enough capacity to cover ' + 'update_horizon and stack_size.') + + logging.info( + 'Creating a %s replay memory with the following parameters:', + self.__class__.__name__) + logging.info('\t timesteps: %d', timesteps) + logging.info('\t replay_capacity: %d', replay_capacity) + logging.info('\t batch_size: %d', batch_size) + logging.info('\t update_horizon: %d', update_horizon) + logging.info('\t gamma: %f', gamma) + + self._disk_saving = save_dir is not None + self._save_dir = save_dir + self._purge_replay_on_shutdown = purge_replay_on_shutdown + if self._disk_saving: + logging.info('\t saving to disk: %s', self._save_dir) + os.makedirs(save_dir, exist_ok=True) + else: + logging.info('\t saving to RAM') + + + self._action_shape = action_shape + self._action_dtype = action_dtype + self._reward_shape = reward_shape + self._reward_dtype = reward_dtype + self._timesteps = timesteps + self._replay_capacity = replay_capacity + self._batch_size = batch_size + self._update_horizon = update_horizon + self._gamma = gamma + self._max_sample_attempts = max_sample_attempts + + self._observation_elements = observation_elements + self._extra_replay_elements = extra_replay_elements + + self._storage_signature, self._obs_signature = self.get_storage_signature() + self._create_storage() + + self._lock = Lock() + self._add_count = mp.Value('i', 0) + + self._replay_capacity = replay_capacity + + self.invalid_range = np.zeros((self._timesteps)) + + # When the horizon is > 1, we compute the sum of discounted rewards as a dot + # product using the precomputed vector . + self._cumulative_discount_vector = np.array( + [math.pow(self._gamma, n) for n in range(update_horizon)], + dtype=np.float32) + + @property + def timesteps(self): + return self._timesteps + + @property + def replay_capacity(self): + return self._replay_capacity + + @property + def batch_size(self): + return self._batch_size + + def _create_storage(self, store=None): + """Creates the numpy arrays used to store transitions. + """ + self._store = {} if store is None else store + for storage_element in self._storage_signature: + array_shape = [self._replay_capacity] + list(storage_element.shape) + if storage_element.name == TERMINAL: + self._store[storage_element.name] = np.full( + array_shape, -1, dtype=storage_element.type) + elif not self._disk_saving: + # If saving to disk, we don't need to store anything else. + self._store[storage_element.name] = np.empty( + array_shape, dtype=storage_element.type) + + def get_storage_signature(self) -> Tuple[List[ReplayElement], + List[ReplayElement]]: + """Returns a default list of elements to be stored in this replay memory. + + Note - Derived classes may return a different signature. + + Returns: + dict of ReplayElements defining the type of the contents stored. + """ + storage_elements = [ + ReplayElement(ACTION, self._action_shape, self._action_dtype), + ReplayElement(REWARD, self._reward_shape, self._reward_dtype), + ReplayElement(TERMINAL, (), np.int8), + ReplayElement(TIMEOUT, (), bool), + ] + + obs_elements = [] + for obs_element in self._observation_elements: + obs_elements.append( + ReplayElement( + obs_element.name, obs_element.shape, obs_element.type)) + storage_elements.extend(obs_elements) + + for extra_replay_element in self._extra_replay_elements: + storage_elements.append(extra_replay_element) + + return storage_elements, obs_elements + + def add(self, action, reward, terminal, timeout, **kwargs): + """Adds a transition to the replay memory. + + WE ONLY STORE THE TPS1s on the final frame + + This function checks the types and handles the padding at the beginning of + an episode. Then it calls the _add function. + + Since the next_observation in the transition will be the observation added + next there is no need to pass it. + + If the replay memory is at capacity the oldest transition will be discarded. + + Args: + action: int, the action in the transition. + reward: float, the reward received in the transition. + terminal: A uint8 acting as a boolean indicating whether the transition + was terminal (1) or not (0). + **kwargs: The remaining args + """ + + # If previous transition was a terminal, then add_final wasn't called + # if not self.is_empty() and self._store['terminal'][self.cursor() - 1] == 1: + # raise ValueError('The previous transition was a terminal, ' + # 'but add_final was not called.') + + kwargs[ACTION] = action + kwargs[REWARD] = reward + kwargs[TERMINAL] = terminal + kwargs[TIMEOUT] = timeout + self._check_add_types(kwargs, self._storage_signature) + self._add(kwargs) + + def add_final(self, **kwargs): + """Adds a transition to the replay memory. + Args: + **kwargs: The remaining args + """ + # if self.is_empty() or self._store['terminal'][self.cursor() - 1] != 1: + # raise ValueError('The previous transition was not terminal.') + self._check_add_types(kwargs, self._obs_signature) + transition = self._final_transition(kwargs) + self._add(transition) + + def _final_transition(self, kwargs): + transition = {} + for element_type in self._storage_signature: + if element_type.name in kwargs: + transition[element_type.name] = kwargs[element_type.name] + elif element_type.name == TERMINAL: + # Used to check that user is correctly adding transitions + transition[element_type.name] = -1 + else: + transition[element_type.name] = np.empty( + element_type.shape, dtype=element_type.type) + return transition + + def _add_initial_to_disk(self ,kwargs: dict): + for i in range(self._timesteps - 1): + with open(join(self._save_dir, '%d.replay' % ( + self._replay_capacity - 1 - i)), 'wb') as f: + pickle.dump(kwargs, f) + + def _add(self, kwargs: dict): + """Internal add method to add to the storage arrays. + + Args: + kwargs: All the elements in a transition. + """ + with self._lock: + cursor = self.cursor() + + if self._disk_saving: + term = self._store[TERMINAL] + term[cursor] = kwargs[TERMINAL] + self._store[TERMINAL] = term + with open(join(self._save_dir, '%d.replay' % cursor), 'wb') as f: + pickle.dump(kwargs, f) + # If first add, then pad for correct wrapping + if self._add_count.value == 0: + self._add_initial_to_disk(kwargs) + else: + for name, data in kwargs.items(): + item = self._store[name] + item[cursor] = data + self._store[name] = item + with self._add_count.get_lock(): + self._add_count.value += 1 + self.invalid_range = invalid_range( + self.cursor(), self._replay_capacity, self._timesteps, + self._update_horizon) + + def _get_from_disk(self, start_index, end_index): + """Returns the range of array at the index handling wraparound if necessary. + + Args: + start_index: int, index to the start of the range to be returned. Range + will wraparound if start_index is smaller than 0. + end_index: int, exclusive end index. Range will wraparound if end_index + exceeds replay_capacity. + + Returns: + np.array, with shape [end_index - start_index, array.shape[1:]]. + """ + assert end_index > start_index, 'end_index must be larger than start_index' + assert end_index >= 0 + assert start_index < self._replay_capacity + if not self.is_full(): + assert end_index <= self.cursor(), ( + 'Index {} has not been added.'.format(start_index)) + + # Here we fake a mini store (buffer) + store = {store_element.name: {} + for store_element in self._storage_signature} + if start_index % self._replay_capacity < end_index % self._replay_capacity: + for i in range(start_index, end_index): + with open(join(self._save_dir, '%d.replay' % i), 'rb') as f: + d = pickle.load(f) + for k, v in d.items(): + store[k][i] = v + else: + for i in range(end_index - start_index): + idx = (start_index + i) % self._replay_capacity + with open(join(self._save_dir, '%d.replay' % idx), 'rb') as f: + d = pickle.load(f) + for k, v in d.items(): + store[k][idx] = v + return store + + def _check_add_types(self, kwargs, signature): + """Checks if args passed to the add method match those of the storage. + + Args: + *args: Args whose types need to be validated. + + Raises: + ValueError: If args have wrong shape or dtype. + """ + + if (len(kwargs)) != len(signature): + expected = str(natsort.natsorted([e.name for e in signature])) + actual = str(natsort.natsorted(list(kwargs.keys()))) + error_list = '\nList of expected:\n{}\nList of actual:\n{}'.format( + expected, actual) + raise ValueError('Add expects {} elements, received {}.'.format( + len(signature), len(kwargs)) + error_list) + + for store_element in signature: + arg_element = kwargs[store_element.name] + if isinstance(arg_element, np.ndarray): + arg_shape = arg_element.shape + elif isinstance(arg_element, tuple) or isinstance(arg_element, list): + # TODO: This is not efficient when arg_element is a list. + arg_shape = np.array(arg_element).shape + else: + # Assume it is scalar. + arg_shape = tuple() + store_element_shape = tuple(store_element.shape) + if arg_shape != store_element_shape: + raise ValueError('arg {} has shape {}, expected {}'.format(store_element.name, + arg_shape, store_element_shape)) + + def is_empty(self): + """Is the Replay Buffer empty?""" + return self._add_count.value == 0 + + def is_full(self): + """Is the Replay Buffer full?""" + return self._add_count.value >= self._replay_capacity + + def cursor(self): + """Index to the location where the next transition will be written.""" + return self._add_count.value % self._replay_capacity + + @property + def add_count(self): + return np.array(self._add_count.value) + + @add_count.setter + def add_count(self, count): + if isinstance(count, int): + self._add_count = mp.Value('i', count) + else: + self._add_count = count + + + def get_range(self, array, start_index, end_index): + """Returns the range of array at the index handling wraparound if necessary. + + Args: + array: np.array, the array to get the stack from. + start_index: int, index to the start of the range to be returned. Range + will wraparound if start_index is smaller than 0. + end_index: int, exclusive end index. Range will wraparound if end_index + exceeds replay_capacity. + + Returns: + np.array, with shape [end_index - start_index, array.shape[1:]]. + """ + assert end_index > start_index, 'end_index must be larger than start_index' + assert end_index >= 0 + assert start_index < self._replay_capacity + if not self.is_full(): + assert end_index <= self.cursor(), ( + 'Index {} has not been added.'.format(start_index)) + + # Fast slice read when there is no wraparound. + if start_index % self._replay_capacity < end_index % self._replay_capacity: + return_array = np.array( + [array[i] for i in range(start_index, end_index)]) + # Slow list read. + else: + indices = [(start_index + i) % self._replay_capacity + for i in range(end_index - start_index)] + return_array = np.array([array[i] for i in indices]) + + return return_array + + def get_range_stack(self, array, start_index, end_index, terminals=None): + """Returns the range of array at the index handling wraparound if necessary. + + Args: + array: np.array, the array to get the stack from. + start_index: int, index to the start of the range to be returned. Range + will wraparound if start_index is smaller than 0. + end_index: int, exclusive end index. Range will wraparound if end_index + exceeds replay_capacity. + + Returns: + np.array, with shape [end_index - start_index, array.shape[1:]]. + """ + return_array = np.array(self.get_range(array, start_index, end_index)) + if terminals is None: + terminals = self.get_range( + self._store[TERMINAL], start_index, end_index) + + terminals = terminals[:-1] + + # Here we now check if we need to pad the front episodes + # If any have a terminal of -1, then we have spilled over + # into the the previous transition + if np.any(terminals == -1): + padding_item = return_array[-1] + _array = list(return_array)[:-1] + arr_len = len(_array) + pad_from_now = False + for i, (ar, term) in enumerate( + zip(reversed(_array), reversed(terminals))): + if term == -1 or pad_from_now: + # The first time we see a -1 term, means we have hit the + # beginning of this episode, so pad from now. + # pad_from_now needed because the next transition (reverse) + # will not be a -1 terminal. + pad_from_now = True + return_array[arr_len - 1 - i] = padding_item + else: + # After we hit out first -1 terminal, we never reassign. + padding_item = ar + + return return_array + + def _get_element_stack(self, array, index, terminals=None): + state = self.get_range_stack(array, + index - self._timesteps + 1, index + 1, + terminals=terminals) + return state + + def get_terminal_stack(self, index): + terminal_stack = self.get_range(self._store[TERMINAL], + index - self._timesteps + 1, + index + 1) + return terminal_stack + + def is_valid_transition(self, index): + """Checks if the index contains a valid transition. + + Checks for collisions with the end of episodes and the current position + of the cursor. + + Args: + index: int, the index to the state in the transition. + + Returns: + Is the index valid: Boolean. + + """ + # Check the index is in the valid range + if index < 0 or index >= self._replay_capacity: + return False + if not self.is_full(): + # The indices and next_indices must be smaller than the cursor. + if index >= self.cursor() - self._update_horizon: + return False + + # Skip transitions that straddle the cursor. + if index in set(self.invalid_range): + return False + + term_stack = self.get_terminal_stack(index) + if term_stack[-1] == -1: + return False + + return True + + def _create_batch_arrays(self, batch_size): + """Create a tuple of arrays with the type of get_transition_elements. + + When using the WrappedReplayBuffer with staging enabled it is important + to create new arrays every sample because StaginArea keeps a pointer to + the returned arrays. + + Args: + batch_size: (int) number of transitions returned. If None the default + batch_size will be used. + + Returns: + Tuple of np.arrays with the shape and type of get_transition_elements. + """ + transition_elements = self.get_transition_elements(batch_size) + batch_arrays = [] + for element in transition_elements: + batch_arrays.append(np.empty(element.shape, dtype=element.type)) + return tuple(batch_arrays) + + def sample_index_batch(self, batch_size): + """Returns a batch of valid indices sampled uniformly. + + Args: + batch_size: int, number of indices returned. + + Returns: + list of ints, a batch of valid indices sampled uniformly. + + Raises: + RuntimeError: If the batch was not constructed after maximum number of + tries. + """ + if self.is_full(): + # add_count >= self._replay_capacity > self._stack_size + min_id = (self.cursor() - self._replay_capacity + + self._timesteps - 1) + max_id = self.cursor() - self._update_horizon + else: + min_id = 0 + max_id = self.cursor() - self._update_horizon + if max_id <= min_id: + raise RuntimeError( + 'Cannot sample a batch with fewer than stack size ' + '({}) + update_horizon ({}) transitions.'. + format(self._timesteps, self._update_horizon)) + + indices = [] + attempt_count = 0 + while (len(indices) < batch_size and + attempt_count < self._max_sample_attempts): + index = np.random.randint(min_id, max_id) % self._replay_capacity + if self.is_valid_transition(index): + indices.append(index) + else: + attempt_count += 1 + if len(indices) != batch_size: + raise RuntimeError( + 'Max sample attempts: Tried {} times but only sampled {}' + ' valid indices. Batch size is {}'. + format(self._max_sample_attempts, len(indices), batch_size)) + + return indices + + def unpack_transition(self, transition_tensors, transition_type): + """Unpacks the given transition into member variables. + + Args: + transition_tensors: tuple of tf.Tensors. + transition_type: tuple of ReplayElements matching transition_tensors. + """ + self.transition = collections.OrderedDict() + for element, element_type in zip(transition_tensors, transition_type): + self.transition[element_type.name] = element + return self.transition + + def sample_transition_batch(self, batch_size=None, indices=None, + pack_in_dict=True): + """Returns a batch of transitions (including any extra contents). + + If get_transition_elements has been overridden and defines elements not + stored in self._store, an empty array will be returned and it will be + left to the child class to fill it. For example, for the child class + OutOfGraphPrioritizedReplayBuffer, the contents of the + sampling_probabilities are stored separately in a sum tree. + + When the transition is terminal next_state_batch has undefined contents. + + NOTE: This transition contains the indices of the sampled elements. + These are only valid during the call to sample_transition_batch, + i.e. they may be used by subclasses of this replay buffer but may + point to different data as soon as sampling is done. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + indices: None or list of ints, the indices of every transition in the + batch. If None, sample the indices uniformly. + + Returns: + transition_batch: tuple of np.arrays with the shape and type as in + get_transition_elements(). + + Raises: + ValueError: If an element to be sampled is missing from the + replay buffer. + """ + + if batch_size is None: + batch_size = self._batch_size + with self._lock: + if indices is None: + indices = self.sample_index_batch(batch_size) + assert len(indices) == batch_size + + transition_elements = self.get_transition_elements(batch_size) + batch_arrays = self._create_batch_arrays(batch_size) + + for batch_element, state_index in enumerate(indices): + + if not self.is_valid_transition(state_index): + raise ValueError('Invalid index %d.' % state_index) + + trajectory_indices = [(state_index + j) % self._replay_capacity + for j in range(self._update_horizon)] + trajectory_terminals = self._store['terminal'][ + trajectory_indices] + is_terminal_transition = trajectory_terminals.any() + if not is_terminal_transition: + trajectory_length = self._update_horizon + else: + # np.argmax of a bool array returns index of the first True. + trajectory_length = np.argmax( + trajectory_terminals.astype(bool), + 0) + 1 + + next_state_index = state_index + trajectory_length + + store = self._store + if self._disk_saving: + store = self._get_from_disk( + state_index - (self._timesteps - 1), + next_state_index + 1) + + trajectory_discount_vector = ( + self._cumulative_discount_vector[:trajectory_length]) + trajectory_rewards = self.get_range(store['reward'], + state_index, + next_state_index) + + terminal_stack = self.get_terminal_stack(state_index) + terminal_stack_tp1 = self.get_terminal_stack( + next_state_index % self._replay_capacity) + + # Fill the contents of each array in the sampled batch. + assert len(transition_elements) == len(batch_arrays) + for element_array, element in zip(batch_arrays, + transition_elements): + if element.is_observation: + if element.name.endswith('tp1'): + element_array[ + batch_element] = self._get_element_stack( + store[element.name[:-4]], + next_state_index % self._replay_capacity, + terminal_stack_tp1) + else: + element_array[ + batch_element] = self._get_element_stack( + store[element.name], + state_index, terminal_stack) + elif element.name == REWARD: + # compute discounted sum of rewards in the trajectory. + element_array[batch_element] = np.sum( + trajectory_discount_vector * trajectory_rewards, + axis=0) + elif element.name == TERMINAL: + element_array[batch_element] = is_terminal_transition + elif element.name == INDICES: + element_array[batch_element] = state_index + elif element.name in store.keys(): + element_array[batch_element] = ( + store[element.name][state_index]) + + if pack_in_dict: + batch_arrays = self.unpack_transition( + batch_arrays, transition_elements) + + # TODO(Mohit): proper fix to discard task names + if 'task' in batch_arrays: + del batch_arrays['task'] + if 'task_tp1' in batch_arrays: + del batch_arrays['task_tp1'] + + return batch_arrays + + def get_transition_elements(self, batch_size=None): + """Returns a 'type signature' for sample_transition_batch. + + Args: + batch_size: int, number of transitions returned. If None, the default + batch_size will be used. + Returns: + signature: A namedtuple describing the method's return type signature. + """ + batch_size = self._batch_size if batch_size is None else batch_size + + transition_elements = [ + ReplayElement(ACTION, (batch_size,) + self._action_shape, + self._action_dtype), + ReplayElement(REWARD, (batch_size,) + self._reward_shape, + self._reward_dtype), + ReplayElement(TERMINAL, (batch_size,), np.int8), + ReplayElement(TIMEOUT, (batch_size,), bool), + ReplayElement(INDICES, (batch_size,), np.int32), + ] + + for element in self._observation_elements: + transition_elements.append(ReplayElement( + element.name, + (batch_size, self._timesteps) + tuple(element.shape), + element.type, True)) + transition_elements.append(ReplayElement( + element.name + '_tp1', + (batch_size, self._timesteps) + tuple(element.shape), + element.type, True)) + + for element in self._extra_replay_elements: + transition_elements.append(ReplayElement( + element.name, + (batch_size,) + tuple(element.shape), + element.type)) + return transition_elements + + def shutdown(self): + if self._purge_replay_on_shutdown: + # Safely delete replay + logging.info('Clearing disk replay buffer.') + for f in [f for f in os.listdir(self._save_dir) if '.replay' in f]: + os.remove(join(self._save_dir, f)) + + def using_disk(self): + return self._disk_saving diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/wrappers/__init__.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/wrappers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7ce7ddb8f0dd8aebd14acc4d5da79d900bd7a4ed --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/wrappers/__init__.py @@ -0,0 +1,24 @@ +from abc import ABC, abstractmethod +from typing import Any + +from yarr.replay_buffer.replay_buffer import ReplayBuffer + + +class WrappedReplayBuffer(ABC): + + def __init__(self, replay_buffer: ReplayBuffer): + """Initializes WrappedReplayBuffer. + + Raises: + ValueError: If update_horizon is not positive. + ValueError: If discount factor is not in [0, 1]. + """ + self._replay_buffer = replay_buffer + + @property + def replay_buffer(self): + return self._replay_buffer + + @abstractmethod + def dataset(self) -> Any: + pass \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/wrappers/pytorch_replay_buffer.py b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/wrappers/pytorch_replay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..2b9f6761324b0e71e98ad07a4e452edcf4841843 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/replay_buffer/wrappers/pytorch_replay_buffer.py @@ -0,0 +1,82 @@ +import time +from threading import Thread + +from torch.utils.data import IterableDataset, DataLoader + +from yarr.replay_buffer.replay_buffer import ReplayBuffer +from yarr.replay_buffer.wrappers import WrappedReplayBuffer + + +class PyTorchIterableReplayDataset(IterableDataset): + + def __init__(self, replay_buffer: ReplayBuffer): + self._replay_buffer = replay_buffer + + def _generator(self): + while True: + yield self._replay_buffer.sample_transition_batch(pack_in_dict=True) + + def __iter__(self): + return iter(self._generator()) + +# class PyTorchIterableReplayDataset(IterableDataset): +# +# BUFFER = 4 +# +# def __init__(self, replay_buffer: ReplayBuffer, num_workers: int): +# self._replay_buffer = replay_buffer +# self._num_wokers = num_workers +# self._samples = [] +# self._lock = Lock() +# +# def _run(self): +# while True: +# # Check if replay buffer is ig enough to be sampled +# while self._replay_buffer.add_count < self._replay_buffer.batch_size: +# time.sleep(1.) +# s = self._replay_buffer.sample_transition_batch(pack_in_dict=True) +# while len(self._samples) >= PyTorchIterableReplayDataset.BUFFER: +# time.sleep(0.25) +# with self._lock: +# self._samples.append(s) +# +# def _generator(self): +# ts = [Thread( +# target=self._run, args=()) for _ in range(self._num_wokers)] +# [t.start() for t in ts] +# while True: +# while len(self._samples) == 0: +# time.sleep(0.1) +# with self._lock: +# s = self._samples.pop(0) +# yield s +# +# def __iter__(self): +# i = iter(self._generator()) +# return i + + +class PyTorchReplayBuffer(WrappedReplayBuffer): + """Wrapper of OutOfGraphReplayBuffer with an in graph sampling mechanism. + + Usage: + To add a transition: call the add function. + + To sample a batch: Construct operations that depend on any of the + tensors is the transition dictionary. Every sess.run + that requires any of these tensors will sample a new + transition. + """ + + def __init__(self, replay_buffer: ReplayBuffer, num_workers: int = 2): + super(PyTorchReplayBuffer, self).__init__(replay_buffer) + self._num_workers = num_workers + + def dataset(self, batch_size=None, drop_last=False) -> DataLoader: + # d = PyTorchIterableReplayDataset(self._replay_buffer) + d = PyTorchIterableReplayDataset(self._replay_buffer) + + # Batch size None disables automatic batching + return DataLoader(d, batch_size=batch_size, + drop_last=drop_last, + num_workers=self._num_workers, pin_memory=True) diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/__init__.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/_env_runner.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/_env_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..a4d9ebe9ae8a9e599041405b1df68e2a7a5ac545 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/_env_runner.py @@ -0,0 +1,228 @@ +import copy +import logging +import os +import time +import pandas as pd + +from multiprocessing import Process, Manager +from multiprocessing import get_start_method, set_start_method +from typing import Any + +import numpy as np +import torch +from yarr.agents.agent import Agent +from yarr.agents.agent import ScalarSummary +from yarr.agents.agent import Summary +from yarr.envs.env import Env +from yarr.utils.rollout_generator import RolloutGenerator +from yarr.utils.log_writer import LogWriter +from yarr.utils.process_str import change_case +from yarr.utils.video_utils import CircleCameraMotion, TaskRecorder + +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +try: + if get_start_method() != 'spawn': + set_start_method('spawn', force=True) +except RuntimeError: + pass + + +class _EnvRunner(object): + + def __init__(self, + train_env: Env, + eval_env: Env, + agent: Agent, + timesteps: int, + train_envs: int, + eval_envs: int, + rollout_episodes: int, + eval_episodes: int, + training_iterations: int, + eval_from_eps_number: int, + episode_length: int, + kill_signal: Any, + step_signal: Any, + num_eval_episodes_signal: Any, + eval_epochs_signal: Any, + eval_report_signal: Any, + log_freq: int, + rollout_generator: RolloutGenerator, + save_load_lock, + current_replay_ratio, + target_replay_ratio, + weightsdir: str = None, + logdir: str = None, + env_device: torch.device = None, + previous_loaded_weight_folder: str = '', + num_eval_runs: int = 1, + ): + self._train_env = train_env + self._eval_env = eval_env + self._agent = agent + self._train_envs = train_envs + self._eval_envs = eval_envs + self._rollout_episodes = rollout_episodes + self._eval_episodes = eval_episodes + self._training_iterations = training_iterations + self._num_eval_runs = num_eval_runs + self._eval_from_eps_number = eval_from_eps_number + self._episode_length = episode_length + self._rollout_generator = rollout_generator + self._weightsdir = weightsdir + self._logdir = logdir + self._env_device = env_device + self._previous_loaded_weight_folder = previous_loaded_weight_folder + + self._timesteps = timesteps + + self._p_args = {} + self.p_failures = {} + manager = Manager() + self.write_lock = manager.Lock() + self.stored_transitions = manager.list() + self.agent_summaries = manager.list() + self._kill_signal = kill_signal + self._step_signal = step_signal + self._num_eval_episodes_signal = num_eval_episodes_signal + self._eval_epochs_signal = eval_epochs_signal + self._eval_report_signal = eval_report_signal + self._save_load_lock = save_load_lock + self._current_replay_ratio = current_replay_ratio + self._target_replay_ratio = target_replay_ratio + self._log_freq = log_freq + + self._new_weights = False + + def restart_process(self, name: str): + p = Process(target=self._run_env, args=self._p_args[name], name=name) + p.start() + return p + + def spin_up_envs(self, name: str, num_envs: int, eval: bool): + + ps = [] + for i in range(num_envs): + n = name + str(i) + self._p_args[n] = (n, eval) + self.p_failures[n] = 0 + p = Process(target=self._run_env, args=self._p_args[n], name=n) + p.start() + ps.append(p) + return ps + + def _load_save(self): + if self._weightsdir is None: + logging.info("'weightsdir' was None, so not loading weights.") + return + while True: + weight_folders = [] + with self._save_load_lock: + if os.path.exists(self._weightsdir): + weight_folders = os.listdir(self._weightsdir) + if len(weight_folders) > 0: + weight_folders = sorted(map(int, weight_folders)) + # Only load if there has been a new weight saving + if self._previous_loaded_weight_folder != weight_folders[-1]: + self._previous_loaded_weight_folder = weight_folders[-1] + d = os.path.join(self._weightsdir, str(weight_folders[-1])) + try: + self._agent.load_weights(d) + except FileNotFoundError: + # Rare case when agent hasn't finished writing. + time.sleep(1) + self._agent.load_weights(d) + print('Agent %s: Loaded weights: %s' % (self._name, d)) + self._new_weights = True + else: + self._new_weights = False + break + print('Waiting for weights to become available.') + time.sleep(1) + + def _get_type(self, x): + if x.dtype == np.float64: + return np.float32 + return x.dtype + + def _get_task_name(self): + if hasattr(self._eval_env, '_task_class'): + eval_task_name = change_case(self._eval_env._task_class.__name__) + multi_task = False + elif hasattr(self._eval_env, '_task_classes'): + if self._eval_env.active_task_id != -1: + task_id = (self._eval_env.active_task_id) % len(self._eval_env._task_classes) + eval_task_name = change_case(self._eval_env._task_classes[task_id].__name__) + else: + eval_task_name = '' + multi_task = True + else: + raise Exception('Neither task_class nor task_classes found in eval env') + return eval_task_name, multi_task + + def _run_env(self, name: str, eval: bool): + + self._name = name + + self._agent = copy.deepcopy(self._agent) + + self._agent.build(training=False, device=self._env_device) + + logging.info('%s: Launching env.' % name) + np.random.seed() + + logging.info('Agent information:') + logging.info(self._agent) + + env = self._train_env + if eval: + env = self._eval_env + env.eval = eval + env.launch() + for ep in range(self._rollout_episodes): + self._load_save() + logging.debug('%s: Starting episode %d.' % (name, ep)) + episode_rollout = [] + generator = self._rollout_generator.generator( + self._step_signal, env, self._agent, + self._episode_length, self._timesteps, + eval, eval_demo_seed=eval_demo_seed, + record_enabled=rec_cfg.enabled) + try: + for replay_transition in generator: + while True: + if self._kill_signal.value: + env.shutdown() + return + if (eval or self._target_replay_ratio is None or + self._step_signal.value <= 0 or ( + self._current_replay_ratio.value > + self._target_replay_ratio)): + break + time.sleep(1) + logging.debug( + 'Agent. Waiting for replay_ratio %f to be more than %f' % + (self._current_replay_ratio.value, self._target_replay_ratio)) + + with self.write_lock: + if len(self.agent_summaries) == 0: + # Only store new summaries if the previous ones + # have been popped by the main env runner. + for s in self._agent.act_summaries(): + self.agent_summaries.append(s) + episode_rollout.append(replay_transition) + except StopIteration as e: + continue + except Exception as e: + env.shutdown() + raise e + + with self.write_lock: + for transition in episode_rollout: + self.stored_transitions.append((name, transition, eval)) + env.shutdown() + + def kill(self): + self._kill_signal.value = True diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/_independent_env_runner.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/_independent_env_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..1fb2c962f465883e06ca9181b94e8c1ac47d7d08 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/_independent_env_runner.py @@ -0,0 +1,296 @@ +import copy +import logging +import os +import time +import pandas as pd + +from multiprocessing import Process, Manager +from multiprocessing import get_start_method, set_start_method +from typing import Any + +import numpy as np +import torch +from yarr.agents.agent import Agent +from yarr.agents.agent import ScalarSummary +from yarr.agents.agent import Summary +from yarr.envs.env import Env +from yarr.utils.rollout_generator import RolloutGenerator +from yarr.utils.log_writer import LogWriter +from yarr.utils.process_str import change_case +from yarr.utils.video_utils import CircleCameraMotion, TaskRecorder + +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +from yarr.runners._env_runner import _EnvRunner +from tqdm import tqdm + +class _IndependentEnvRunner(_EnvRunner): + + def __init__(self, + train_env: Env, + eval_env: Env, + agent: Agent, + timesteps: int, + train_envs: int, + eval_envs: int, + rollout_episodes: int, + eval_episodes: int, + training_iterations: int, + eval_from_eps_number: int, + episode_length: int, + kill_signal: Any, + step_signal: Any, + num_eval_episodes_signal: Any, + eval_epochs_signal: Any, + eval_report_signal: Any, + log_freq: int, + rollout_generator: RolloutGenerator, + save_load_lock, + current_replay_ratio, + target_replay_ratio, + weightsdir: str = None, + logdir: str = None, + env_device: torch.device = None, + previous_loaded_weight_folder: str = '', + num_eval_runs: int = 1, + ): + + super().__init__(train_env, eval_env, agent, timesteps, + train_envs, eval_envs, rollout_episodes, eval_episodes, + training_iterations, eval_from_eps_number, episode_length, + kill_signal, step_signal, num_eval_episodes_signal, + eval_epochs_signal, eval_report_signal, log_freq, + rollout_generator, save_load_lock, current_replay_ratio, + target_replay_ratio, weightsdir, logdir, env_device, + previous_loaded_weight_folder, num_eval_runs) + + def _load_save(self): + if self._weightsdir is None: + logging.info("'weightsdir' was None, so not loading weights.") + return + while True: + weight_folders = [] + with self._save_load_lock: + if os.path.exists(self._weightsdir): + weight_folders = os.listdir(self._weightsdir) + if len(weight_folders) > 0: + weight_folders = sorted(map(int, weight_folders)) + # only load if there has been a new weight saving + if self._previous_loaded_weight_folder != weight_folders[-1]: + self._previous_loaded_weight_folder = weight_folders[-1] + d = os.path.join(self._weightsdir, str(weight_folders[-1])) + try: + self._agent.load_weights(d) + except FileNotFoundError: + # rare case when agent hasn't finished writing. + time.sleep(1) + self._agent.load_weights(d) + logging.info('Agent %s: Loaded weights: %s' % (self._name, d)) + self._new_weights = True + else: + self._new_weights = False + break + logging.info('Waiting for weights to become available.') + time.sleep(1) + + def _get_task_name(self): + if hasattr(self._eval_env, '_task_class'): + eval_task_name = change_case(self._eval_env._task_class.__name__) + multi_task = False + elif hasattr(self._eval_env, '_task_classes'): + if self._eval_env.active_task_id != -1: + task_id = (self._eval_env.active_task_id) % len(self._eval_env._task_classes) + eval_task_name = change_case(self._eval_env._task_classes[task_id].__name__) + else: + eval_task_name = '' + multi_task = True + else: + raise Exception('Neither task_class nor task_classes found in eval env') + return eval_task_name, multi_task + + def _run_eval_independent(self, name: str, + stats_accumulator, + weight, + writer_lock, + eval=True, + device_idx=0, + save_metrics=True, + cinematic_recorder_cfg=None,): + self._name = name + self._save_metrics = save_metrics + self._is_test_set = type(weight) == dict + + self._agent = copy.deepcopy(self._agent) + + device = torch.device('cuda:%d' % device_idx) if torch.cuda.device_count() > 1 else torch.device('cuda:0') + with writer_lock: # hack to prevent multiple CLIP downloads ... argh should use a separate lock + self._agent.build(training=False, device=device) + + logging.info('%s: Launching env.' % name) + np.random.seed() + + logging.info('Agent information:') + logging.info(self._agent) + + env = self._eval_env + env.eval = eval + env.launch() + + # initialize cinematic recorder if specified + rec_cfg = cinematic_recorder_cfg + if rec_cfg.enabled: + cam_placeholder = Dummy('cam_cinematic_placeholder') + cam = VisionSensor.create(rec_cfg.camera_resolution) + cam.set_pose(cam_placeholder.get_pose()) + cam.set_parent(cam_placeholder) + + cam_motion = CircleCameraMotion(cam, Dummy('cam_cinematic_base'), rec_cfg.rotate_speed) + tr = TaskRecorder(env, cam_motion, fps=rec_cfg.fps) + + env.env._action_mode.arm_action_mode.set_callable_each_step(tr.take_snap) + + if not os.path.exists(self._weightsdir): + raise Exception('No weights directory found.') + + # to save or not to save evaluation metrics (set as False for recording videos) + if self._save_metrics: + csv_file = 'eval_data.csv' if not self._is_test_set else 'test_data.csv' + writer = LogWriter(self._logdir, True, True, + env_csv=csv_file) + + # one weight for all tasks (used for validation) + if type(weight) == int: + logging.info('Evaluating weight %s' % weight) + weight_path = os.path.join(self._weightsdir, str(weight)) + seed_path = self._weightsdir.replace('/weights', '') + self._agent.load_weights(weight_path) + weight_name = str(weight) + + new_transitions = {'train_envs': 0, 'eval_envs': 0} + total_transitions = {'train_envs': 0, 'eval_envs': 0} + current_task_id = -1 + + for n_eval in tqdm(range(self._num_eval_runs),mininterval=10, desc="Tasks"): + if rec_cfg.enabled: + tr._cam_motion.save_pose() + + # best weight for each task (used for test evaluation) + if type(weight) == dict: + task_name = list(weight.keys())[n_eval] + task_weight = weight[task_name] + weight_path = os.path.join(self._weightsdir, str(task_weight)) + seed_path = self._weightsdir.replace('/weights', '') + self._agent.load_weights(weight_path) + weight_name = str(task_weight) + print('Evaluating weight %s for %s' % (weight_name, task_name)) + + # evaluate on N tasks * M episodes per task = total eval episodes + for ep in tqdm(range(self._eval_episodes),desc="Episodes"): + eval_demo_seed = ep + self._eval_from_eps_number + logging.info('%s: Starting episode %d, seed %d.' % (name, ep, eval_demo_seed)) + + # the current task gets reset after every M episodes + episode_rollout = [] + generator = self._rollout_generator.generator( + self._step_signal, env, self._agent, + self._episode_length, self._timesteps, + eval, eval_demo_seed=eval_demo_seed, + record_enabled=rec_cfg.enabled, + ) + try: + for replay_transition in generator: + while True: + if self._kill_signal.value: + env.shutdown() + return + if (eval or self._target_replay_ratio is None or + self._step_signal.value <= 0 or ( + self._current_replay_ratio.value > + self._target_replay_ratio)): + break + time.sleep(1) + logging.debug( + 'Agent. Waiting for replay_ratio %f to be more than %f' % + (self._current_replay_ratio.value, self._target_replay_ratio)) + + with self.write_lock: + if len(self.agent_summaries) == 0: + # Only store new summaries if the previous ones + # have been popped by the main env runner. + for s in self._agent.act_summaries(): + self.agent_summaries.append(s) + episode_rollout.append(replay_transition) + except StopIteration as e: + continue + except Exception as e: + env.shutdown() + raise e + + with self.write_lock: + for transition in episode_rollout: + self.stored_transitions.append((name, transition, eval)) + + new_transitions['eval_envs'] += 1 + total_transitions['eval_envs'] += 1 + stats_accumulator.step(transition, eval) + current_task_id = transition.info['active_task_id'] + + self._num_eval_episodes_signal.value += 1 + + task_name, _ = self._get_task_name() + reward = episode_rollout[-1].reward + lang_goal = env._lang_goal + print(f"Evaluating {task_name} | Episode {ep} | Score: {reward} | Lang Goal: {lang_goal}") + # save recording + if rec_cfg.enabled: + success = reward > 0.99 + record_file = os.path.join(seed_path, 'videos', + '%s_w%s_s%s_%s.mp4' % (task_name, + weight_name, + eval_demo_seed, + 'succ' if success else 'fail')) + + lang_goal = self._eval_env._lang_goal + + tr.save(record_file, lang_goal, reward) + tr._cam_motion.restore_pose() + + # report summaries + summaries = [] + summaries.extend(stats_accumulator.pop()) + + eval_task_name, multi_task = self._get_task_name() + + if eval_task_name and multi_task: + for s in summaries: + if 'eval' in s.name: + s.name = '%s/%s' % (s.name, eval_task_name) + + if len(summaries) > 0: + if multi_task: + task_score = [s.value for s in summaries if f'eval_envs/return/{eval_task_name}' in s.name][0] + else: + task_score = [s.value for s in summaries if f'eval_envs/return' in s.name][0] + else: + task_score = "unknown" + + print(f"Finished {eval_task_name} | Final Score: {task_score}\n") + + if self._save_metrics: + with writer_lock: + writer.add_summaries(weight_name, summaries) + + self._new_transitions = {'train_envs': 0, 'eval_envs': 0} + self.agent_summaries[:] = [] + self.stored_transitions[:] = [] + + if self._save_metrics: + with writer_lock: + writer.end_iteration() + + logging.info('Finished evaluation.') + env.shutdown() + + def kill(self): + self._kill_signal.value = True diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/env_runner.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/env_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..fe229c37df5e3b8bf29b1b8b62c26f67800201af --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/env_runner.py @@ -0,0 +1,224 @@ +import collections +import logging +import os +import signal +import time +from multiprocessing import Value +from threading import Thread +from typing import List +from typing import Union + +import numpy as np +import torch +from yarr.agents.agent import Agent +from yarr.agents.agent import ScalarSummary +from yarr.agents.agent import Summary +from yarr.envs.env import Env +from yarr.replay_buffer.replay_buffer import ReplayBuffer +from yarr.runners._env_runner import _EnvRunner +from yarr.utils.rollout_generator import RolloutGenerator +from yarr.utils.stat_accumulator import StatAccumulator, SimpleAccumulator +from yarr.utils.process_str import change_case +from helpers.custom_rlbench_env import CustomRLBenchEnv, CustomMultiTaskRLBenchEnv + +class EnvRunner(object): + + def __init__(self, + train_env: Env, + agent: Agent, + train_replay_buffer: Union[ReplayBuffer, List[ReplayBuffer]], + num_train_envs: int, + num_eval_envs: int, + rollout_episodes: int, + eval_episodes: int, + training_iterations: int, + eval_from_eps_number: int, + episode_length: int, + eval_env: Union[Env, None] = None, + eval_replay_buffer: Union[ReplayBuffer, List[ReplayBuffer], None] = None, + stat_accumulator: Union[StatAccumulator, None] = None, + rollout_generator: RolloutGenerator = None, + weightsdir: str = None, + logdir: str = None, + max_fails: int = 10, + num_eval_runs: int = 1, + env_device: torch.device = None, + multi_task: bool = False): + self._train_env = train_env + self._eval_env = eval_env if eval_env else train_env + self._agent = agent + self._train_envs = num_train_envs + self._eval_envs = num_eval_envs + self._train_replay_buffer = train_replay_buffer if isinstance(train_replay_buffer, list) else [train_replay_buffer] + self._timesteps = self._train_replay_buffer[0].timesteps if self._train_replay_buffer[0] is not None else 1 + + if eval_replay_buffer is not None: + eval_replay_buffer = eval_replay_buffer if isinstance(eval_replay_buffer, list) else [eval_replay_buffer] + self._eval_replay_buffer = eval_replay_buffer + self._rollout_episodes = rollout_episodes + self._eval_episodes = eval_episodes + self._num_eval_runs = num_eval_runs + self._training_iterations = training_iterations + self._eval_from_eps_number = eval_from_eps_number + self._episode_length = episode_length + self._stat_accumulator = stat_accumulator + self._rollout_generator = ( + RolloutGenerator() if rollout_generator is None + else rollout_generator) + self._rollout_generator._env_device = env_device + self._weightsdir = weightsdir + self._logdir = logdir + self._max_fails = max_fails + self._env_device = env_device + self._previous_loaded_weight_folder = '' + self._p = None + self._kill_signal = Value('b', 0) + self._step_signal = Value('i', -1) + self._num_eval_episodes_signal = Value('i', 0) + self._eval_epochs_signal = Value('i', 0) + self._eval_report_signal = Value('b', 0) + self._new_transitions = {'train_envs': 0, 'eval_envs': 0} + self._total_transitions = {'train_envs': 0, 'eval_envs': 0} + self.log_freq = 1000 # Will get overridden later + self.target_replay_ratio = None # Will get overridden later + self.current_replay_ratio = Value('f', -1) + self._current_task_id = -1 + self._multi_task = multi_task + + def summaries(self) -> List[Summary]: + summaries = [] + if self._stat_accumulator is not None: + summaries.extend(self._stat_accumulator.pop()) + for key, value in self._new_transitions.items(): + summaries.append(ScalarSummary('%s/new_transitions' % key, value)) + for key, value in self._total_transitions.items(): + summaries.append(ScalarSummary('%s/total_transitions' % key, value)) + self._new_transitions = {'train_envs': 0, 'eval_envs': 0} + summaries.extend(self._agent_summaries) + + # add current task_name to eval summaries .... argh this should be inside a helper function + if hasattr(self._eval_env, '_task_class'): + eval_task_name = change_case(self._eval_env._task_class.__name__) + elif hasattr(self._eval_env, '_task_classes'): + if self._current_task_id != -1: + task_id = (self._current_task_id) % len(self._eval_env._task_classes) + eval_task_name = change_case(self._eval_env._task_classes[task_id].__name__) + else: + eval_task_name = '' + else: + raise Exception('Neither task_class nor task_classes found in eval env') + + # multi-task summaries + if eval_task_name and self._multi_task: + for s in summaries: + if 'eval' in s.name: + s.name = '%s/%s' % (s.name, eval_task_name) + + return summaries + + def _update(self): + # Move the stored transitions to the replay and accumulate statistics. + new_transitions = collections.defaultdict(int) + with self._internal_env_runner.write_lock: + self._agent_summaries = list( + self._internal_env_runner.agent_summaries) + if self._num_eval_episodes_signal.value % self._eval_episodes == 0 and self._num_eval_episodes_signal.value > 0: + self._internal_env_runner.agent_summaries[:] = [] + for name, transition, eval in self._internal_env_runner.stored_transitions: + add_to_buffer = (not eval) or self._eval_replay_buffer is not None + if add_to_buffer: + kwargs = dict(transition.observation) + replay_index = transition.info["active_task_id"] + rb = self._eval_replay_buffer[replay_index] if eval else self._train_replay_buffer[replay_index] + rb.add( + np.array(transition.action), transition.reward, + transition.terminal, + transition.timeout, **kwargs) + if transition.terminal: + rb.add_final( + **transition.final_observation) + new_transitions[name] += 1 + self._new_transitions[ + 'eval_envs' if eval else 'train_envs'] += 1 + self._total_transitions[ + 'eval_envs' if eval else 'train_envs'] += 1 + if self._stat_accumulator is not None: + self._stat_accumulator.step(transition, eval) + self._current_task_id = transition.info["active_task_id"] if eval else -1 + self._internal_env_runner.stored_transitions[:] = [] # Clear list + return new_transitions + + def _run(self, save_load_lock): + self._internal_env_runner = _EnvRunner( + self._train_env, self._eval_env, self._agent, self._timesteps, self._train_envs, + self._eval_envs, self._rollout_episodes, self._eval_episodes, + self._training_iterations, self._eval_from_eps_number, self._episode_length, self._kill_signal, + self._step_signal, self._num_eval_episodes_signal, + self._eval_epochs_signal, self._eval_report_signal, + self.log_freq, self._rollout_generator, save_load_lock, + self.current_replay_ratio, self.target_replay_ratio, + self._weightsdir, self._logdir, + self._env_device, self._previous_loaded_weight_folder, + num_eval_runs=self._num_eval_runs) + training_envs = self._internal_env_runner.spin_up_envs('train_env', self._train_envs, False) + eval_envs = self._internal_env_runner.spin_up_envs('eval_env', self._eval_envs, True) + envs = training_envs + eval_envs + no_transitions = {env.name: 0 for env in envs} + while True: + for p in envs: + if p.exitcode is not None: + envs.remove(p) + if p.exitcode != 0: + self._internal_env_runner.p_failures[p.name] += 1 + n_failures = self._internal_env_runner.p_failures[p.name] + if n_failures > self._max_fails: + logging.error('Env %s failed too many times (%d times > %d)' % + (p.name, n_failures, self._max_fails)) + raise RuntimeError('Too many process failures.') + logging.warning('Env %s failed (%d times <= %d). restarting' % + (p.name, n_failures, self._max_fails)) + p = self._internal_env_runner.restart_process(p.name) + envs.append(p) + + if not self._kill_signal.value: + new_transitions = self._update() + for p in envs: + if new_transitions[p.name] == 0: + no_transitions[p.name] += 1 + else: + no_transitions[p.name] = 0 + if no_transitions[p.name] > 1200: #600: # 10min + logging.warning("Env %s hangs, so restarting" % p.name) + envs.remove(p) + os.kill(p.pid, signal.SIGTERM) + p = self._internal_env_runner.restart_process(p.name) + envs.append(p) + no_transitions[p.name] = 0 + + if len(envs) == 0: + break + time.sleep(1) + + def start(self, save_load_lock): + self._p = Thread(target=self._run, args=(save_load_lock,), daemon=True) + self._p.name = 'EnvRunnerThread' + self._p.start() + + def wait(self): + if self._p.is_alive(): + self._p.join() + + def stop(self): + if self._p.is_alive(): + self._kill_signal.value = True + self._p.join() + + def set_step(self, step): + self._step_signal.value = step + + def set_eval_report(self, report): + self._eval_report_signal.value = report + + def set_eval_epochs(self, epochs): + self._eval_epochs_signal.value = epochs + diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/independent_env_runner.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/independent_env_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..cb2dc9bc07b48766c5236282b5e6cef438bdc3a7 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/independent_env_runner.py @@ -0,0 +1,129 @@ +import numpy as np +import torch +from typing import List +from typing import Union +from yarr.agents.agent import Agent +from yarr.envs.env import Env +from yarr.replay_buffer.replay_buffer import ReplayBuffer +from yarr.runners._independent_env_runner import _IndependentEnvRunner +from yarr.utils.rollout_generator import RolloutGenerator +from yarr.utils.stat_accumulator import StatAccumulator, SimpleAccumulator +from yarr.agents.agent import Summary +from helpers.custom_rlbench_env import CustomRLBenchEnv, CustomMultiTaskRLBenchEnv + +from yarr.runners.env_runner import EnvRunner + +class IndependentEnvRunner(EnvRunner): + + def __init__(self, + train_env: Env, + agent: Agent, + train_replay_buffer: Union[ReplayBuffer, List[ReplayBuffer]], + num_train_envs: int, + num_eval_envs: int, + rollout_episodes: int, + eval_episodes: int, + training_iterations: int, + eval_from_eps_number: int, + episode_length: int, + eval_env: Union[Env, None] = None, + eval_replay_buffer: Union[ReplayBuffer, List[ReplayBuffer], None] = None, + stat_accumulator: Union[StatAccumulator, None] = None, + rollout_generator: RolloutGenerator = None, + weightsdir: str = None, + logdir: str = None, + max_fails: int = 10, + num_eval_runs: int = 1, + env_device: torch.device = None, + multi_task: bool = False, + ): + super().__init__(train_env, agent, train_replay_buffer, num_train_envs, num_eval_envs, + rollout_episodes, eval_episodes, training_iterations, eval_from_eps_number, + episode_length, eval_env, eval_replay_buffer, stat_accumulator, + rollout_generator, weightsdir, logdir, max_fails, num_eval_runs, + env_device, multi_task) + + def summaries(self) -> List[Summary]: + summaries = [] + if self._stat_accumulator is not None: + summaries.extend(self._stat_accumulator.pop()) + self._new_transitions = {'train_envs': 0, 'eval_envs': 0} + summaries.extend(self._agent_summaries) + + # add current task_name to eval summaries .... argh this should be inside a helper function + if hasattr(self._eval_env, '_task_class'): + eval_task_name = change_case(self._eval_env._task_class.__name__) + elif hasattr(self._eval_env, '_task_classes'): + if self._current_task_id != -1: + task_id = (self._current_task_id) % len(self._eval_env._task_classes) + eval_task_name = change_case(self._eval_env._task_classes[task_id].__name__) + else: + eval_task_name = '' + else: + raise Exception('Neither task_class nor task_classes found in eval env') + + # multi-task summaries + if eval_task_name and self._multi_task: + for s in summaries: + if 'eval' in s.name: + s.name = '%s/%s' % (s.name, eval_task_name) + + return summaries + + # serialized evaluator for individual tasks + def start(self, weight, + save_load_lock, writer_lock, + env_config, + device_idx, + save_metrics, + cinematic_recorder_cfg): + if hasattr(self, "_on_thread_start"): + self._on_thread_start() + + multi_task = isinstance(env_config[0], list) + if multi_task: + eval_env = CustomMultiTaskRLBenchEnv( + task_classes=env_config[0], + observation_config=env_config[1], + action_mode=env_config[2], + dataset_root=env_config[3], + episode_length=env_config[4], + headless=env_config[5], + swap_task_every=env_config[6], + include_lang_goal_in_obs=env_config[7], + time_in_state=env_config[8], + record_every_n=env_config[9]) + else: + eval_env = CustomRLBenchEnv( + task_class=env_config[0], + observation_config=env_config[1], + action_mode=env_config[2], + dataset_root=env_config[3], + episode_length=env_config[4], + headless=env_config[5], + include_lang_goal_in_obs=env_config[6], + time_in_state=env_config[7], + record_every_n=env_config[8]) + + self._internal_env_runner = _IndependentEnvRunner( + self._train_env, eval_env, self._agent, self._timesteps, self._train_envs, + self._eval_envs, self._rollout_episodes, self._eval_episodes, + self._training_iterations, self._eval_from_eps_number, self._episode_length, self._kill_signal, + self._step_signal, self._num_eval_episodes_signal, + self._eval_epochs_signal, self._eval_report_signal, + self.log_freq, self._rollout_generator, None, + self.current_replay_ratio, self.target_replay_ratio, + self._weightsdir, self._logdir, + self._env_device, self._previous_loaded_weight_folder, + num_eval_runs=self._num_eval_runs) + + stat_accumulator = SimpleAccumulator(eval_video_fps=30) + self._internal_env_runner._run_eval_independent('eval_env', + stat_accumulator, + weight, + writer_lock, + True, + device_idx, + save_metrics, + cinematic_recorder_cfg, + ) diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/offline_train_runner.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/offline_train_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..3ae4e2d30f53e8497db126784d4f5006d1108dc1 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/offline_train_runner.py @@ -0,0 +1,195 @@ +import copy +import logging +import os +import shutil +import time +from typing import List +from typing import Union + +import psutil +import torch +from yarr.agents.agent import Agent +from yarr.replay_buffer.wrappers.pytorch_replay_buffer import \ + PyTorchReplayBuffer +from yarr.utils.log_writer import LogWriter +from yarr.utils.stat_accumulator import StatAccumulator +from tqdm import tqdm +from omegaconf import DictConfig +import wandb +from termcolor import cprint + +from yarr.runners.weight_init_utils import resolve_initial_weight_state + + +class OfflineTrainRunner(): + + def __init__(self, + agent: Agent, + wrapped_replay_buffer: PyTorchReplayBuffer, + train_device: torch.device, + stat_accumulator: Union[StatAccumulator, None] = None, + iterations: int = int(6e6), + logdir: str = '/tmp/yarr/logs', + logging_level: int = logging.INFO, + log_freq: int = 10, + weightsdir: str = '/tmp/yarr/weights', + num_weights_to_keep: int = 60, + save_freq: int = 100, + tensorboard_logging: bool = True, + csv_logging: bool = False, + load_existing_weights: bool = True, + rank: int = None, + world_size: int = None, + cfg: DictConfig = None, + ): + self._agent = agent + self._wrapped_buffer = wrapped_replay_buffer + self._stat_accumulator = stat_accumulator + self._iterations = iterations + self._logdir = logdir + self._logging_level = logging_level + self._log_freq = log_freq + self._weightsdir = weightsdir + self._num_weights_to_keep = num_weights_to_keep + self._save_freq = save_freq + + self._wrapped_buffer = wrapped_replay_buffer + self._train_device = train_device + self._tensorboard_logging = tensorboard_logging + self._csv_logging = csv_logging + self._load_existing_weights = load_existing_weights + self._rank = rank + self._world_size = world_size + + self.method = cfg.method + self.use_wandb = cfg.framework.use_wandb + self.anybimanual = cfg.framework.anybimanual + if self.use_wandb and rank == 0: + wandb_name = cfg.framework.wandb_name + wandb.init(project=cfg.framework.wandb_project, group=str(cfg.framework.wandb_group), name=wandb_name, config=cfg) + cprint(f'[wandb] init in {cfg.framework.wandb_project}/{cfg.framework.wandb_group}/{wandb_name}', 'cyan') + + self._writer = None + if logdir is None: + logging.info("'logdir' was None. No logging will take place.") + else: + self._writer = LogWriter( + self._logdir, tensorboard_logging, csv_logging) + + if weightsdir is None: + logging.info( + "'weightsdir' was None. No weight saving will take place.") + else: + os.makedirs(self._weightsdir, exist_ok=True) + + def _save_model(self, i): + d = os.path.join(self._weightsdir, str(i)) + os.makedirs(d, exist_ok=True) + self._agent.save_weights(d) + + # remove oldest save + prev_dir = os.path.join(self._weightsdir, str( + i - self._save_freq * self._num_weights_to_keep)) + if os.path.exists(prev_dir): + shutil.rmtree(prev_dir) + + def _step(self, i, sampled_batch): + update_dict = self._agent.update(i, sampled_batch) + total_losses = update_dict['total_losses'] + return total_losses + + def _get_resume_eval_epoch(self): + starting_epoch = 0 + eval_csv_file = self._weightsdir.replace('weights', 'eval_data.csv') # TODO(mohit): check if it's supposed be 'env_data.csv' + if os.path.exists(eval_csv_file): + import pandas as pd + eval_dict = pd.read_csv(eval_csv_file).to_dict() + epochs = list(eval_dict['step'].values()) + return epochs[-1] if len(epochs) > 0 else starting_epoch + else: + return starting_epoch + + def start(self): + + if hasattr(self, "_on_thread_start"): + self._on_thread_start() + else: + logging.getLogger().setLevel(self._logging_level) + + + self._agent = copy.deepcopy(self._agent) + self._agent.build(training=True, device=self._train_device) + + pretrained_dir = None + if self.anybimanual: + method = self.method + pretrained_dir = os.environ.get("ANYBIMANUAL_PRETRAINED_WEIGHTS_DIR") + if not pretrained_dir: + pretrained_dir = f"/path/to/your/pre-trained ckpts/{method.name}/" + + start_iter, initial_weight_dir, initial_weight_source = resolve_initial_weight_state( + weightsdir=self._weightsdir, + load_existing_weights=self._load_existing_weights, + anybimanual=self.anybimanual, + pretrained_dir=pretrained_dir, + ) + if initial_weight_dir is not None: + self._agent.load_weights(initial_weight_dir) + if self._rank == 0: + if initial_weight_source == "resume": + logging.info(f"Resuming training from iteration {start_iter} ...") + elif initial_weight_source == "pretrained": + print("-----------------Loading Success Pre-trained Model----------------------") + logging.info("Loading Success Pre-trained Model") + + dataset = self._wrapped_buffer.dataset() + data_iter = iter(dataset) + + process = psutil.Process(os.getpid()) + num_cpu = psutil.cpu_count() + # start_iter = 0 + for i in tqdm(range(start_iter, self._iterations), mininterval=10): + log_iteration = i % self._log_freq == 0 and i > 0 + + if log_iteration: + process.cpu_percent(interval=None) + + t = time.time() + sampled_batch = next(data_iter) + sample_time = time.time() - t + + batch = {k: v.to(self._train_device) for k, v in sampled_batch.items() if type(v) == torch.Tensor} + t = time.time() + loss = self._step(i, batch) + step_time = time.time() - t + + if self._rank == 0: + if log_iteration and self._writer is not None: + # agent_summaries = self._agent.update_summaries() + # self._writer.add_summaries(i, agent_summaries) + + # self._writer.add_scalar( + # i, 'monitoring/memory_gb', + # process.memory_info().rss * 1e-9) + # self._writer.add_scalar( + # i, 'monitoring/cpu_percent', + # process.cpu_percent(interval=None) / num_cpu) + + logging.info(f"Train Step {i:06d} | Loss: {loss:0.5f} | Sample time: {sample_time:0.6f} | Step time: {step_time:0.4f}.") + + # self._writer.end_iteration() + + if i % self._save_freq == 0 and self._weightsdir is not None: + self._save_model(i) + torch.cuda.empty_cache() + + if self._rank == 0 and self._weightsdir is not None: + final_weights_dir = os.path.join(self._weightsdir, str(self._iterations)) + if not os.path.exists(final_weights_dir): + self._save_model(self._iterations) + + if self._rank == 0 and self._writer is not None: + self._writer.close() + logging.info('Stopping envs ...') + + # self._wrapped_buffer.replay_buffer.shutdown() diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/pytorch_train_runner.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/pytorch_train_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..910aed49c2031bdef4ecf6e972346446a6902d2a --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/pytorch_train_runner.py @@ -0,0 +1,308 @@ +import copy +import logging +import os +import shutil +import signal +import sys +import threading +import time +from multiprocessing import Lock +from typing import Optional, List +from typing import Union + +import gc +import numpy as np +import psutil +import torch +import pandas as pd +from yarr.agents.agent import Agent +from yarr.replay_buffer.wrappers.pytorch_replay_buffer import \ + PyTorchReplayBuffer +from yarr.runners.env_runner import EnvRunner +from yarr.runners.train_runner import TrainRunner +from yarr.utils.log_writer import LogWriter +from yarr.utils.stat_accumulator import StatAccumulator +from yarr.replay_buffer.prioritized_replay_buffer import PrioritizedReplayBuffer + +NUM_WEIGHTS_TO_KEEP = 60 + + +class PyTorchTrainRunner(TrainRunner): + + def __init__(self, + agent: Agent, + env_runner: EnvRunner, + wrapped_replay_buffer: Union[ + PyTorchReplayBuffer, List[PyTorchReplayBuffer]], + train_device: torch.device, + replay_buffer_sample_rates: List[float] = None, + stat_accumulator: Union[StatAccumulator, None] = None, + iterations: int = int(1e6), + num_train_envs: int = 1, + num_eval_envs: int = 1, + eval_episodes: int = 10, + logdir: str = '/tmp/yarr/logs', + log_freq: int = 10, + transitions_before_train: int = 1000, + weightsdir: str = '/tmp/yarr/weights', + save_freq: int = 100, + replay_ratio: Optional[float] = None, + tensorboard_logging: bool = True, + csv_logging: bool = False, + buffers_per_batch: int = -1, # -1 = all + load_existing_weights: bool = True): + super(PyTorchTrainRunner, self).__init__( + agent, env_runner, wrapped_replay_buffer, + stat_accumulator, + iterations, logdir, log_freq, transitions_before_train, weightsdir, + save_freq) + + env_runner.log_freq = log_freq + env_runner.target_replay_ratio = replay_ratio + self._wrapped_buffer = wrapped_replay_buffer if isinstance( + wrapped_replay_buffer, list) else [wrapped_replay_buffer] + self._replay_buffer_sample_rates = ( + [1.0] if replay_buffer_sample_rates is None else + replay_buffer_sample_rates) + if len(self._replay_buffer_sample_rates) != len(wrapped_replay_buffer): + logging.warning( + 'Numbers of replay buffers differs from sampling rates. Setting as uniform sampling.') + self._replay_buffer_sample_rates = [1.0 / len(self._wrapped_buffer)] * len(self._wrapped_buffer) + if sum(self._replay_buffer_sample_rates) != 1: + raise ValueError('Sum of sampling rates should be 1.') + + self._train_device = train_device + self._tensorboard_logging = tensorboard_logging + self._csv_logging = csv_logging + self._num_train_envs = num_train_envs + self._num_eval_envs = num_eval_envs + self._eval_episodes = eval_episodes + self._load_existing_weights = load_existing_weights + + if replay_ratio is not None and replay_ratio < 0: + raise ValueError("max_replay_ratio must be positive.") + self._target_replay_ratio = replay_ratio + + self._writer = None + if logdir is None: + logging.info("'logdir' was None. No logging will take place.") + else: + self._writer = LogWriter( + self._logdir, tensorboard_logging, csv_logging) + if weightsdir is None: + logging.info( + "'weightsdir' was None. No weight saving will take place.") + else: + os.makedirs(self._weightsdir, exist_ok=True) + self._buffers_per_batch = buffers_per_batch if buffers_per_batch > 0 else len(wrapped_replay_buffer) + + def _save_model(self, i): + with self._save_load_lock: + d = os.path.join(self._weightsdir, str(i)) + os.makedirs(d, exist_ok=True) + self._agent.save_weights(d) + # Remove oldest save + prev_dir = os.path.join(self._weightsdir, str( + i - self._save_freq * NUM_WEIGHTS_TO_KEEP)) + if os.path.exists(prev_dir): + shutil.rmtree(prev_dir) + + def _step(self, i, sampled_batch): + update_dict = self._agent.update(i, sampled_batch) + if "priority" in update_dict: + priority = update_dict['priority'].cpu().detach().numpy() if isinstance(update_dict['priority'], torch.Tensor) else np.numpy(update_dict['priority']) + else: + priority = None + indices = sampled_batch['indices'].cpu().detach().numpy() + acc_bs = 0 + for wb_idx, wb in enumerate(self._wrapped_buffer): + bs = wb.replay_buffer.batch_size + if 'priority' in update_dict: + indices_ = indices[:, wb_idx] + if hasattr(wb, "replay_buffer"): + if len(priority.shape) > 1: + priority_ = priority[:, wb_idx] + else: + # legacy version + priority_ = priority[acc_bs: acc_bs + bs] + wb.replay_buffer.set_priority(indices_, priority_) + acc_bs += bs + + def _signal_handler(self, sig, frame): + if threading.current_thread().name != 'MainThread': + return + logging.info('SIGINT captured. Shutting down.' + 'This may take a few seconds.') + self._env_runner.stop() + [r.replay_buffer.shutdown() for r in self._wrapped_buffer] + sys.exit(0) + + def _get_add_counts(self): + return np.array([ + r.replay_buffer.add_count for r in self._wrapped_buffer]) + + def _get_sum_add_counts(self): + return sum([ + r.replay_buffer.add_count for r in self._wrapped_buffer]) + + def _get_resume_eval_epoch(self): + starting_epoch = 0 + eval_csv_file = self._weightsdir.replace('weights', 'eval_data.csv') # TODO(mohit): check if it's supposed be 'env_data.csv' + if os.path.exists(eval_csv_file): + eval_dict = pd.read_csv(eval_csv_file).to_dict() + epochs = list(eval_dict['step'].values()) + return epochs[-1] if len(epochs) > 0 else starting_epoch + else: + return starting_epoch + + def start(self): + + signal.signal(signal.SIGINT, self._signal_handler) + + self._save_load_lock = Lock() + + # Kick off the environments + self._env_runner.start(self._save_load_lock) + + self._agent = copy.deepcopy(self._agent) + self._agent.build(training=True, device=self._train_device) + + if self._weightsdir is not None: + existing_weights = sorted([int(f) for f in os.listdir(self._weightsdir)]) + if (not self._load_existing_weights) or len(existing_weights) == 0: + self._save_model(0) + start_iter = 0 + else: + resume_iteration = existing_weights[-1] + self._agent.load_weights(os.path.join(self._weightsdir, str(resume_iteration))) + start_iter = resume_iteration + 1 + print(f"Resuming training from iteration {resume_iteration} ...") + + if self._num_eval_envs > 0: + eval_epoch = self._get_resume_eval_epoch() + self._env_runner.set_eval_epochs(eval_epoch) + self._writer.set_resumed_from_prev_run(True) + print(f"Resuming evaluation from epoch {eval_epoch} ...") + + while (np.any(self._get_add_counts() < self._transitions_before_train)): + time.sleep(1) + logging.info( + 'Waiting for %d samples before training. Currently have %s.' % + (self._transitions_before_train, str(self._get_add_counts()))) + + datasets = [r.dataset() for r in self._wrapped_buffer] + data_iter = [iter(d) for d in datasets] + + init_replay_size = self._get_sum_add_counts().astype(float) + batch_times_buffers_per_sample = sum([ + r.replay_buffer.batch_size for r in self._wrapped_buffer[:self._buffers_per_batch]]) + process = psutil.Process(os.getpid()) + num_cpu = psutil.cpu_count() + + for i in range(start_iter, self._iterations): + self._env_runner.set_step(i) + + if self._num_train_envs > 0 or self._num_eval_envs == 0: + log_iteration = i % self._log_freq == 0 and i > 0 + else: + num_eval_episodes = self._env_runner._num_eval_episodes_signal.value + log_iteration = self._env_runner._eval_report_signal.value and num_eval_episodes > 0 + + if log_iteration: + process.cpu_percent(interval=None) + + def get_replay_ratio(): + size_used = batch_times_buffers_per_sample * i + size_added = ( + self._get_sum_add_counts() + - init_replay_size + ) + replay_ratio = size_used / (size_added + 1e-6) + return replay_ratio + + if self._target_replay_ratio is not None: + # wait for env_runner collecting enough samples + while True: + replay_ratio = get_replay_ratio() + self._env_runner.current_replay_ratio.value = replay_ratio + if replay_ratio < self._target_replay_ratio: + break + time.sleep(1) + logging.debug( + 'Waiting for replay_ratio %f to be less than %f.' % + (replay_ratio, self._target_replay_ratio)) + del replay_ratio + + t = time.time() + + sampled_task_ids = np.random.choice( + range(len(datasets)), self._buffers_per_batch, replace=False) + sampled_batch = [next(data_iter[j]) for j in sampled_task_ids] + result = {} + for key in sampled_batch[0]: + result[key] = torch.stack([d[key] for d in sampled_batch], 1) + sampled_batch = result + sample_time = time.time() - t + + batch = {k: v.to(self._train_device) for k, v in sampled_batch.items()} + t = time.time() + self._step(i, batch) + step_time = time.time() - t + + if log_iteration and self._writer is not None: + replay_ratio = get_replay_ratio() + logging.info('Train Step %d. Eval Epoch %d. Sample time: %s. Step time: %s. Replay ratio: %s.' % ( + i, self._env_runner._eval_epochs_signal.value, sample_time, step_time, replay_ratio)) + agent_summaries = self._agent.update_summaries() + env_summaries = self._env_runner.summaries() + + # agent summaries + self._writer.add_summaries(i, agent_summaries) + + # env summaries + self._writer.add_summaries(self._env_runner._eval_epochs_signal.value, env_summaries) + + for r_i, wrapped_buffer in enumerate(self._wrapped_buffer): + self._writer.add_scalar( + i, 'replay%d/add_count' % r_i, + wrapped_buffer.replay_buffer.add_count) + self._writer.add_scalar( + i, 'replay%d/size' % r_i, + wrapped_buffer.replay_buffer.replay_capacity + if wrapped_buffer.replay_buffer.is_full() + else wrapped_buffer.replay_buffer.add_count) + + self._writer.add_scalar( + i, 'replay/replay_ratio', replay_ratio) + self._writer.add_scalar( + i, 'replay/update_to_insert_ratio', + float(i) / float( + self._get_sum_add_counts() - + init_replay_size + 1e-6)) + + self._writer.add_scalar( + i, 'monitoring/sample_time_per_item', + sample_time / batch_times_buffers_per_sample) + self._writer.add_scalar( + i, 'monitoring/train_time_per_item', + step_time / batch_times_buffers_per_sample) + self._writer.add_scalar( + i, 'monitoring/memory_gb', + process.memory_info().rss * 1e-9) + self._writer.add_scalar( + i, 'monitoring/cpu_percent', + process.cpu_percent(interval=None) / num_cpu) + + self._env_runner.set_eval_report(False) + + self._writer.end_iteration() + + if i % self._save_freq == 0 and self._weightsdir is not None: + self._save_model(i) + + if self._writer is not None: + self._writer.close() + + logging.info('Stopping envs ...') + self._env_runner.stop() + [r.replay_buffer.shutdown() for r in self._wrapped_buffer] diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/train_runner.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/train_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..38c7ce87ee06bbcee824d06c0066c8e4d113b37a --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/train_runner.py @@ -0,0 +1,37 @@ +from abc import abstractmethod, ABC +from typing import Union, List + +from yarr.agents.agent import Agent +from yarr.replay_buffer.wrappers import WrappedReplayBuffer +from yarr.runners.env_runner import EnvRunner +from yarr.utils.stat_accumulator import StatAccumulator + + +class TrainRunner(ABC): + + def __init__(self, + agent: Agent, + env_runner: EnvRunner, + wrapped_replay_buffer: WrappedReplayBuffer, + stat_accumulator: Union[StatAccumulator, None] = None, + iterations: int = int(1e6), + logdir: str = '/tmp/yarr/logs', + log_freq: int = 500, + transitions_before_train: int = 1000, + weightsdir: str = '/tmp/yarr/weights', + save_freq: int = 100, + ): + self._agent = agent + self._env_runner = env_runner + self._wrapped_buffer = wrapped_replay_buffer + self._stat_accumulator = stat_accumulator + self._iterations = iterations + self._logdir = logdir + self._log_freq = log_freq + self._transitions_before_train = transitions_before_train + self._weightsdir = weightsdir + self._save_freq = save_freq + + @abstractmethod + def start(self): + pass diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/runners/weight_init_utils.py b/third_party/AnyBimanual/third_party/YARR/yarr/runners/weight_init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4ab8787790fcf5419d937920b4785d41fc868a43 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/runners/weight_init_utils.py @@ -0,0 +1,32 @@ +import os + + +def resolve_initial_weight_state( + weightsdir: str, + load_existing_weights: bool, + anybimanual: bool, + pretrained_dir: str = None, +): + existing_weights = [] + if weightsdir and os.path.isdir(weightsdir): + existing_weights = sorted( + int(f) + for f in os.listdir(weightsdir) + if f.isdigit() and os.path.isdir(os.path.join(weightsdir, f)) + ) + + if load_existing_weights and existing_weights: + resume_iteration = existing_weights[-1] + return resume_iteration, os.path.join(weightsdir, str(resume_iteration)), "resume" + + if anybimanual: + if not pretrained_dir: + pretrained_dir = "/path/to/your/pre-trained ckpts/" + if not os.path.isdir(pretrained_dir): + raise FileNotFoundError( + "AnyBimanual pretrained weights directory not found: " + f"{pretrained_dir}. Set ANYBIMANUAL_PRETRAINED_WEIGHTS_DIR to a valid release checkpoint directory." + ) + return 0, pretrained_dir, "pretrained" + + return 0, None, None diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/__init__.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/log_writer.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/log_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..163bcd3c8ecc37205e1954c7bf34f55d7189cf73 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/log_writer.py @@ -0,0 +1,128 @@ +import csv +import logging +import os +from collections import OrderedDict + +import numpy as np +import torch +from yarr.agents.agent import ScalarSummary, HistogramSummary, ImageSummary, \ + VideoSummary, TextSummary +from torch.utils.tensorboard import SummaryWriter + + +class LogWriter(object): + + def __init__(self, + logdir: str, + tensorboard_logging: bool, + csv_logging: bool, + train_csv: str = 'train_data.csv', + env_csv: str = 'env_data.csv'): + self._tensorboard_logging = tensorboard_logging + self._csv_logging = csv_logging + os.makedirs(logdir, exist_ok=True) + if tensorboard_logging: + self._tf_writer = SummaryWriter(logdir) + if csv_logging: + self._train_prev_row_data = self._train_row_data = OrderedDict() + self._train_csv_file = os.path.join(logdir, train_csv) + self._env_prev_row_data = self._env_row_data = OrderedDict() + self._env_csv_file = os.path.join(logdir, env_csv) + self._train_field_names = None + self._env_field_names = None + + def add_scalar(self, i, name, value): + if self._tensorboard_logging: + self._tf_writer.add_scalar(name, value, i) + if self._csv_logging: + if 'env' in name or 'eval' in name or 'test' in name: + if len(self._env_row_data) == 0: + self._env_row_data['step'] = i + self._env_row_data[name] = value.item() if isinstance( + value, torch.Tensor) else value + else: + if len(self._train_row_data) == 0: + self._train_row_data['step'] = i + self._train_row_data[name] = value.item() if isinstance( + value, torch.Tensor) else value + + def add_summaries(self, i, summaries): + for summary in summaries: + try: + if isinstance(summary, ScalarSummary): + self.add_scalar(i, summary.name, summary.value) + elif self._tensorboard_logging: + if isinstance(summary, HistogramSummary): + self._tf_writer.add_histogram( + summary.name, summary.value, i) + elif isinstance(summary, ImageSummary): + # Only grab first item in batch + v = (summary.value if summary.value.ndim == 3 else + summary.value[0]) + self._tf_writer.add_image(summary.name, v, i) + elif isinstance(summary, VideoSummary): + # Only grab first item in batch + v = (summary.value if summary.value.ndim == 5 else + np.array([summary.value])) + self._tf_writer.add_video( + summary.name, v, i, fps=summary.fps) + elif isinstance(summary, TextSummary): + self._tf_writer.add_text(summary.name, summary.value, i) + except Exception as e: + logging.error('Error on summary: %s' % summary.name) + raise e + + def end_iteration(self): + # write train data + if self._csv_logging and len(self._train_row_data) > 0: + should_write_train_header = not os.path.exists(self._train_csv_file) + with open(self._train_csv_file, mode='a+') as csv_f: + names = self._train_row_data.keys() + writer = csv.DictWriter(csv_f, fieldnames=names) + if should_write_train_header: + if self._train_field_names is None: + writer.writeheader() + else: + if not np.array_equal(self._train_field_names, self._train_row_data.keys()): + # Special case when we are logging faster than new + # summaries are coming in. + missing_keys = list(set(self._train_field_names) - set( + self._train_row_data.keys())) + for mk in missing_keys: + self._train_row_data[mk] = self._train_prev_row_data[mk] + self._train_field_names = names + try: + writer.writerow(self._train_row_data) + except Exception as e: + print(e) + self._train_prev_row_data = self._train_row_data + self._train_row_data = OrderedDict() + + # write env data (also eval or test during evaluation) + if self._csv_logging and len(self._env_row_data) > 0: + should_write_env_header = not os.path.exists(self._env_csv_file) + with open(self._env_csv_file, mode='a+') as csv_f: + names = self._env_row_data.keys() + writer = csv.DictWriter(csv_f, fieldnames=names) + if should_write_env_header: + if self._env_field_names is None: + writer.writeheader() + else: + if not np.array_equal(self._env_field_names, self._env_row_data.keys()): + # Special case when we are logging faster than new + # summaries are coming in. + missing_keys = list(set(self._env_field_names) - set( + self._env_row_data.keys())) + for mk in missing_keys: + self._env_row_data[mk] = self._env_prev_row_data[mk] + self._env_field_names = names + try: + writer.writerow(self._env_row_data) + except Exception as e: + print(e) + self._env_prev_row_data = self._env_row_data + self._env_row_data = OrderedDict() + + def close(self): + if self._tensorboard_logging: + self._tf_writer.close() diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/multi_task_rollout_generator.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/multi_task_rollout_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..9c665497123279fe4ea1480c6697da675a38d9c2 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/multi_task_rollout_generator.py @@ -0,0 +1,65 @@ +from multiprocessing import Value + +import numpy as np + +from yarr.agents.agent import Agent +from yarr.envs.env import Env +from yarr.envs.multi_task_env import MultiTaskEnv +from yarr.utils.transition import ReplayTransition + + +class RolloutGenerator(object): + + def _get_type(self, x): + if x.dtype == np.float64: + return np.float32 + return x.dtype + + def generator(self, step_signal: Value, env: MultiTaskEnv, agent: Agent, + episode_length: int, timesteps: int, eval: bool): + obs = env.reset() + agent.reset() + obs_history = {k: [np.array(v, dtype=self._get_type(v))] * timesteps for k, v in obs.items()} + for step in range(episode_length): + + prepped_data = {k: np.array([v]) for k, v in obs_history.items()} + + act_result = agent.act(step_signal.value, prepped_data, + deterministic=eval) + + # Convert to np if not already + agent_obs_elems = {k: np.array(v) for k, v in + act_result.observation_elements.items()} + agent_extra_elems = {k: np.array(v) for k, v in + act_result.replay_elements.items()} + + transition = env.step(act_result) + timeout = False + if step == episode_length - 1: + # If last transition, and not terminal, then we timed out + timeout = not transition.terminal + if timeout: + transition.terminal = True + if "needs_reset" in transition.info: + transition.info["needs_reset"] = True + + obs.update(agent_obs_elems) + obs_tp1 = dict(transition.observation) + + for k in obs_history.keys(): + obs_history[k].append(transition.observation[k]) + obs_history[k].pop(0) + + transition.info["active_task_id"] = env.active_task_id + + replay_transition = ReplayTransition( + obs, act_result.action, transition.reward, + transition.terminal, + timeout, obs_tp1, agent_extra_elems, + transition.info) + + obs = transition.observation + yield replay_transition + + if transition.info.get("needs_reset", transition.terminal): + return diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/observation_type.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/observation_type.py new file mode 100644 index 0000000000000000000000000000000000000000..bc2dd5a86334eb23c10d2e31b16ee062e01703ff --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/observation_type.py @@ -0,0 +1,10 @@ +from typing import Type +import numpy as np + + +class ObservationElement(object): + + def __init__(self, name: str, shape: tuple, type: Type[np.dtype]): + self.name = name + self.shape = shape + self.type = type diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/process_str.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/process_str.py new file mode 100644 index 0000000000000000000000000000000000000000..883f099a7c6e2ce23eed0419094b1d42f6906f2d --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/process_str.py @@ -0,0 +1,5 @@ +from functools import reduce + + +def change_case(str): + return reduce(lambda x, y: x + ('_' if y.isupper() else '') + y, str).lower() \ No newline at end of file diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/rollout_generator.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/rollout_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..f271c8ef7e0620e8989f944c496348994d9599b2 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/rollout_generator.py @@ -0,0 +1,92 @@ +from multiprocessing import Value + +import numpy as np +import torch +from yarr.agents.agent import Agent +from yarr.envs.env import Env +from yarr.utils.transition import ReplayTransition +from helpers import demo_loading_utils, utils + +class RolloutGenerator(object): + + def _get_type(self, x): + if x.dtype == np.float64: + return np.float32 + return x.dtype + + def generator(self, step_signal: Value, env: Env, agent: Agent, + episode_length: int, timesteps: int, + eval: bool, eval_demo_seed: int = 0, + record_enabled: bool = False,): + + if eval: + print("eval_demo_seed: ",eval_demo_seed) + obs = env.reset_to_demo(eval_demo_seed) + else: + obs = env.reset() + agent.reset() + # print(len(obs)) + obs_history = {k: [np.array(v, dtype=self._get_type(v))] * timesteps for k, v in obs.items()} + # print(episode_length) + for step in range(episode_length): + + prepped_data = {k:torch.tensor(np.array(v)[None], device=self._env_device) for k, v in obs_history.items()} + # agent.act + act_result = agent.act(step_signal.value, prepped_data,deterministic=eval) + # print(act_result) + # print("----------act_result---------") + # print(act_result.action) + # Convert to np if not already + agent_obs_elems = {k: np.array(v) for k, v in + act_result.observation_elements.items()} + extra_replay_elements = {k: np.array(v) for k, v in + act_result.replay_elements.items()} + + transition = env.step(act_result) + obs_tp1 = dict(transition.observation) + timeout = False + if step == episode_length - 1: + # If last transition, and not terminal, then we timed out + timeout = not transition.terminal + if timeout: + transition.terminal = True + if "needs_reset" in transition.info: + transition.info["needs_reset"] = True + + obs_and_replay_elems = {} + obs_and_replay_elems.update(obs) + obs_and_replay_elems.update(agent_obs_elems) + obs_and_replay_elems.update(extra_replay_elements) + + for k in obs_history.keys(): + obs_history[k].append(transition.observation[k]) + obs_history[k].pop(0) + + transition.info["active_task_id"] = env.active_task_id + + replay_transition = ReplayTransition( + obs_and_replay_elems, act_result.action, transition.reward, + transition.terminal, timeout, summaries=transition.summaries, + info=transition.info) + + if transition.terminal or timeout: + # If the agent gives us observations then we need to call act + # one last time (i.e. acting in the terminal state). + if len(act_result.observation_elements) > 0: + prepped_data = {k: torch.tensor([v], device=self._env_device) for k, v in obs_history.items()} + act_result = agent.act(step_signal.value, prepped_data, + deterministic=eval) + agent_obs_elems_tp1 = {k: np.array(v) for k, v in + act_result.observation_elements.items()} + obs_tp1.update(agent_obs_elems_tp1) + replay_transition.final_observation = obs_tp1 + + if record_enabled and transition.terminal or timeout or step == episode_length - 1: + env.env._action_mode.arm_action_mode.record_end(env.env._scene, + steps=60, step_scene=True) + + obs = dict(transition.observation) + yield replay_transition + + if transition.info.get("needs_reset", transition.terminal): + return diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/stat_accumulator.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/stat_accumulator.py new file mode 100644 index 0000000000000000000000000000000000000000..3b3d3f16eb9101412425b5e542be82ee72d3c787 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/stat_accumulator.py @@ -0,0 +1,192 @@ +from multiprocessing import Lock +from typing import List + +import numpy as np +from yarr.agents.agent import Summary, ScalarSummary +from yarr.utils.transition import ReplayTransition + + +class StatAccumulator(object): + + def step(self, transition: ReplayTransition, eval: bool): + pass + + def pop(self) -> List[Summary]: + pass + + def peak(self) -> List[Summary]: + pass + + def reset(self) -> None: + pass + + +class Metric(object): + + def __init__(self): + self._previous = [] + self._current = 0 + + def update(self, value): + self._current += value + + def next(self): + self._previous.append(self._current) + self._current = 0 + + def reset(self): + self._previous.clear() + + def min(self): + return np.min(self._previous) + + def max(self): + return np.max(self._previous) + + def mean(self): + return np.mean(self._previous) + + def median(self): + return np.median(self._previous) + + def std(self): + return np.std(self._previous) + + def __len__(self): + return len(self._previous) + + def __getitem__(self, i): + return self._previous[i] + + +class _SimpleAccumulator(StatAccumulator): + + def __init__(self, prefix, eval_video_fps: int = 30, + mean_only: bool = True): + self._prefix = prefix + self._eval_video_fps = eval_video_fps + self._mean_only = mean_only + self._lock = Lock() + self._episode_returns = Metric() + self._episode_lengths = Metric() + self._summaries = [] + self._transitions = 0 + + def _reset_data(self): + with self._lock: + self._episode_returns.reset() + self._episode_lengths.reset() + self._summaries.clear() + + def step(self, transition: ReplayTransition, eval: bool): + with self._lock: + self._transitions += 1 + self._episode_returns.update(transition.reward) + self._episode_lengths.update(1) + if transition.terminal: + self._episode_returns.next() + self._episode_lengths.next() + self._summaries.extend(list(transition.summaries)) + + def _get(self) -> List[Summary]: + sums = [] + + if self._mean_only: + stat_keys = ["mean"] + else: + stat_keys = ["min", "max", "mean", "median", "std"] + names = ["return", "length"] + metrics = [self._episode_returns, self._episode_lengths] + for name, metric in zip(names, metrics): + for stat_key in stat_keys: + if self._mean_only: + assert stat_key == "mean" + sum_name = '%s/%s' % (self._prefix, name) + else: + sum_name = '%s/%s/%s' % (self._prefix, name, stat_key) + sums.append( + ScalarSummary(sum_name, getattr(metric, stat_key)())) + sums.append(ScalarSummary( + '%s/total_transitions' % self._prefix, self._transitions)) + sums.extend(self._summaries) + return sums + + def pop(self) -> List[Summary]: + data = [] + if len(self._episode_returns) > 1: + data = self._get() + self._reset_data() + return data + + def peak(self) -> List[Summary]: + return self._get() + + def reset(self): + self._transitions = 0 + self._reset_data() + + +class SimpleAccumulator(StatAccumulator): + + def __init__(self, eval_video_fps: int = 30, mean_only: bool = True): + self._train_acc = _SimpleAccumulator( + 'train_envs', eval_video_fps, mean_only=mean_only) + self._eval_acc = _SimpleAccumulator( + 'eval_envs', eval_video_fps, mean_only=mean_only) + + def step(self, transition: ReplayTransition, eval: bool): + if eval: + self._eval_acc.step(transition, eval) + else: + self._train_acc.step(transition, eval) + + def pop(self) -> List[Summary]: + return self._train_acc.pop() + self._eval_acc.pop() + + def peak(self) -> List[Summary]: + return self._train_acc.peak() + self._eval_acc.peak() + + def reset(self) -> None: + self._train_acc.reset() + self._eval_acc.reset() + + +class MultiTaskAccumulator(StatAccumulator): + + def __init__(self, num_tasks, + eval_video_fps: int = 30, mean_only: bool = True, + train_prefix: str = 'train_task', + eval_prefix: str = 'eval_task'): + self._train_accs = [_SimpleAccumulator( + '%s%d/envs' % (train_prefix, i), eval_video_fps, mean_only=mean_only) + for i in range(num_tasks)] + self._eval_accs = [_SimpleAccumulator( + '%s%d/envs' % (eval_prefix, i), eval_video_fps, mean_only=mean_only) + for i in range(num_tasks)] + self._train_accs_mean = _SimpleAccumulator( + '%s_summary/envs' % train_prefix, eval_video_fps, + mean_only=mean_only) + + def step(self, transition: ReplayTransition, eval: bool): + replay_index = transition.info["active_task_id"] + if eval: + self._eval_accs[replay_index].step(transition, eval) + else: + self._train_accs[replay_index].step(transition, eval) + self._train_accs_mean.step(transition, eval) + + def pop(self) -> List[Summary]: + combined = self._train_accs_mean.pop() + for acc in self._train_accs + self._eval_accs: + combined.extend(acc.pop()) + return combined + + def peak(self) -> List[Summary]: + combined = self._train_accs_mean.peak() + for acc in self._train_accs + self._eval_accs: + combined.extend(acc.peak()) + return combined + + def reset(self) -> None: + self._train_accs_mean.reset() + [acc.reset() for acc in self._train_accs + self._eval_accs] diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/transition.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/transition.py new file mode 100644 index 0000000000000000000000000000000000000000..1cd4cc4fde733bba7cd088620de91572cdb17788 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/transition.py @@ -0,0 +1,33 @@ +from typing import List + +import numpy as np +from yarr.agents.agent import Summary + + +class Transition(object): + + def __init__(self, observation: dict, reward: float, terminal: bool, + info: dict = None, summaries: List[Summary] = None): + self.observation = observation + self.reward = reward + self.terminal = terminal + self.info = info or {} + self.summaries = summaries or [] + + +class ReplayTransition(object): + + def __init__(self, observation: dict, action: np.ndarray, + reward: float, terminal: bool, timeout: bool, + final_observation: dict = None, + summaries: List[Summary] = None, + info: dict = None): + self.observation = observation + self.action = action + self.reward = reward + self.terminal = terminal + self.timeout = timeout + # final only populated on last timestep + self.final_observation = final_observation + self.summaries = summaries or [] + self.info = info diff --git a/third_party/AnyBimanual/third_party/YARR/yarr/utils/video_utils.py b/third_party/AnyBimanual/third_party/YARR/yarr/utils/video_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e993b021c5aea485b71e96043a4dc41caac8e249 --- /dev/null +++ b/third_party/AnyBimanual/third_party/YARR/yarr/utils/video_utils.py @@ -0,0 +1,78 @@ +import os +import numpy as np +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor +from rlbench import Environment +from rlbench.backend.observation import Observation + + +class CameraMotion(object): + def __init__(self, cam: VisionSensor): + self.cam = cam + + def step(self): + raise NotImplementedError() + + def save_pose(self): + self._prev_pose = self.cam.get_pose() + + def restore_pose(self): + self.cam.set_pose(self._prev_pose) + + +class CircleCameraMotion(CameraMotion): + + def __init__(self, cam: VisionSensor, origin: Dummy, + speed: float, init_rotation: float = np.deg2rad(180)): + super().__init__(cam) + self.origin = origin + self.speed = speed # in radians + self.origin.rotate([0, 0, init_rotation]) + + def step(self): + self.origin.rotate([0, 0, self.speed]) + + +class TaskRecorder(object): + + def __init__(self, env: Environment, cam_motion: CameraMotion, fps=30): + self._env = env + self._cam_motion = cam_motion + self._fps = fps + self._snaps = [] + self._current_snaps = [] + + def take_snap(self, obs: Observation): + self._cam_motion.step() + self._current_snaps.append( + (self._cam_motion.cam.capture_rgb() * 255.).astype(np.uint8)) + + def save(self, path, lang_goal, reward): + print(f"Converting to video ... {path}") + os.makedirs(os.path.dirname(path), exist_ok=True) + # OpenCV QT version can conflict with PyRep, so import here + import cv2 + image_size = self._cam_motion.cam.get_resolution() + video = cv2.VideoWriter( + path, cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), self._fps, + tuple(image_size)) + + for image in self._current_snaps: + frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + font = cv2.FONT_HERSHEY_DUPLEX + font_scale = (0.45 * image_size[0]) / 640 + font_thickness = 2 + + + if lang_goal: + + lang_textsize = cv2.getTextSize(lang_goal, font, font_scale, font_thickness)[0] + lang_textX = (image_size[0] - lang_textsize[0]) // 2 + + frame = cv2.putText(frame, lang_goal, org=(lang_textX, image_size[1] - 35), + fontScale=font_scale, fontFace=font, color=(0, 0, 0), + thickness=font_thickness, lineType=cv2.LINE_AA) + video.write(frame) + video.release() + self._current_snaps = [] diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.circleci/build_count.py b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/build_count.py new file mode 100644 index 0000000000000000000000000000000000000000..aecb54d1bfeeecfcc90570a68552f97011780cec --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/build_count.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Print the number of nightly builds +""" + +from collections import Counter + +import yaml + + +conf = yaml.safe_load(open("config.yml")) +jobs = conf["workflows"]["build_and_test"]["jobs"] + + +def jobtype(job): + if isinstance(job, str): + return job + if len(job) == 1: + [name] = job.keys() + return name + return "MULTIPLE PARTS" + + +for i, j in Counter(map(jobtype, jobs)).items(): + print(i, j) +print() +print(len(jobs)) diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.circleci/check.sh b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/check.sh new file mode 100644 index 0000000000000000000000000000000000000000..bea4614153227769e0ab676ba857a473e32bfc8c --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/check.sh @@ -0,0 +1,13 @@ +#!/bin/bash -e +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# Run this script before committing config.yml to verify it is valid yaml. + +python -c 'import yaml; yaml.safe_load(open("config.yml"))' && echo OK - valid yaml + +msg="circleci not installed so can't check schema" +command -v circleci > /dev/null && (cd ..; circleci config validate) || echo "$msg" diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.circleci/config.in.yml b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/config.in.yml new file mode 100644 index 0000000000000000000000000000000000000000..b32ba66a68c3a8e3bc232e5d2c36024bca799c5e --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/config.in.yml @@ -0,0 +1,171 @@ +version: 2.1 + +#examples: +#https://github.com/facebookresearch/ParlAI/blob/master/.circleci/config.yml +#https://github.com/facebookresearch/hydra/blob/master/.circleci/config.yml +#https://github.com/facebookresearch/habitat-api/blob/master/.circleci/config.yml + +#drive tests with nox or tox or pytest? + +# ------------------------------------------------------------------------------------- +# environments where we run our jobs +# ------------------------------------------------------------------------------------- + + +setupcuda: &setupcuda + run: + name: Setup CUDA + working_directory: ~/ + command: | + # download and install nvidia drivers, cuda, etc + wget --no-verbose --no-clobber -P ~/nvidia-downloads https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.19.01_linux.run + sudo sh ~/nvidia-downloads/cuda_11.3.1_465.19.01_linux.run --silent + echo "Done installing CUDA." + pyenv versions + nvidia-smi + pyenv global 3.9.1 + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release` + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.7)" + type: string + cu_version: + description: "CUDA version to build against, in CU format (e.g., cpu or cu100)" + type: string + wheel_docker_image: + description: "Wheel only: what docker image to use" + type: string + default: "pytorch/manylinux-cuda101" + conda_docker_image: + description: "what docker image to use for docker" + type: string + default: "pytorch/conda-cuda" + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: << parameters.cu_version >> + TESTRUN_DOCKER_IMAGE: << parameters.conda_docker_image >> + +jobs: + main: + environment: + CUDA_VERSION: "11.3" + resource_class: gpu.nvidia.small.multi + machine: + image: linux-cuda-11:default + steps: + - checkout + - <<: *setupcuda + - run: pip3 install --progress-bar off imageio wheel matplotlib 'pillow<7' + - run: pip3 install --progress-bar off torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html + # - run: conda create -p ~/conda_env python=3.7 numpy + # - run: conda activate ~/conda_env + # - run: conda install -c pytorch pytorch torchvision + + - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore' + - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/iopath' + - run: + name: build + command: | + export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64 + python3 setup.py build_ext --inplace + - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64 python -m unittest discover -v -s tests -t . + - run: python3 setup.py bdist_wheel + + binary_linux_wheel: + <<: *binary_common + docker: + - image: << parameters.wheel_docker_image >> + auth: + username: $DOCKERHUB_USERNAME + password: $DOCKERHUB_TOKEN + resource_class: 2xlarge+ + steps: + - checkout + - run: MAX_JOBS=15 packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "<< parameters.conda_docker_image >>" + auth: + username: $DOCKERHUB_USERNAME + password: $DOCKERHUB_TOKEN + resource_class: 2xlarge+ + steps: + - checkout + # This is building with cuda but no gpu present, + # so we aren't running the tests. + - run: + name: build + no_output_timeout: 40m + command: MAX_JOBS=15 TEST_FLAG=--no-test python3 packaging/build_conda.py + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_linux_conda_cuda: + <<: *binary_common + machine: + image: linux-cuda-11:default + resource_class: gpu.nvidia.small.multi + steps: + - checkout + + - run: + name: Pull docker image + command: | + nvidia-smi + set -e + + { docker login -u="$DOCKERHUB_USERNAME" -p="$DOCKERHUB_TOKEN" ; } 2> /dev/null + + echo Pulling docker image $TESTRUN_DOCKER_IMAGE + docker pull $TESTRUN_DOCKER_IMAGE + - run: + name: Build and run tests + no_output_timeout: 40m + command: | + set -e + + cd ${HOME}/project/ + + export JUST_TESTRUN=1 + VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION -e JUST_TESTRUN" + + docker run --gpus all --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${TESTRUN_DOCKER_IMAGE} python3 ./packaging/build_conda.py + +workflows: + version: 2 + build_and_test: + jobs: + # - main: + # context: DOCKERHUB_TOKEN + {{workflows()}} + - binary_linux_conda_cuda: + name: testrun_conda_cuda_py310_cu117_pyt201 + context: DOCKERHUB_TOKEN + python_version: "3.10" + pytorch_version: '2.0.1' + cu_version: "cu117" diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.circleci/config.yml b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..2519f39f3dec732368897c842a8e26ec08fb41e5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/config.yml @@ -0,0 +1,632 @@ +version: 2.1 + +#examples: +#https://github.com/facebookresearch/ParlAI/blob/master/.circleci/config.yml +#https://github.com/facebookresearch/hydra/blob/master/.circleci/config.yml +#https://github.com/facebookresearch/habitat-api/blob/master/.circleci/config.yml + +#drive tests with nox or tox or pytest? + +# ------------------------------------------------------------------------------------- +# environments where we run our jobs +# ------------------------------------------------------------------------------------- + + +setupcuda: &setupcuda + run: + name: Setup CUDA + working_directory: ~/ + command: | + # download and install nvidia drivers, cuda, etc + wget --no-verbose --no-clobber -P ~/nvidia-downloads https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.19.01_linux.run + sudo sh ~/nvidia-downloads/cuda_11.3.1_465.19.01_linux.run --silent + echo "Done installing CUDA." + pyenv versions + nvidia-smi + pyenv global 3.9.1 + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release` + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.7)" + type: string + cu_version: + description: "CUDA version to build against, in CU format (e.g., cpu or cu100)" + type: string + wheel_docker_image: + description: "Wheel only: what docker image to use" + type: string + default: "pytorch/manylinux-cuda101" + conda_docker_image: + description: "what docker image to use for docker" + type: string + default: "pytorch/conda-cuda" + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: << parameters.cu_version >> + TESTRUN_DOCKER_IMAGE: << parameters.conda_docker_image >> + +jobs: + main: + environment: + CUDA_VERSION: "11.3" + resource_class: gpu.nvidia.small.multi + machine: + image: linux-cuda-11:default + steps: + - checkout + - <<: *setupcuda + - run: pip3 install --progress-bar off imageio wheel matplotlib 'pillow<7' + - run: pip3 install --progress-bar off torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html + # - run: conda create -p ~/conda_env python=3.7 numpy + # - run: conda activate ~/conda_env + # - run: conda install -c pytorch pytorch torchvision + + - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore' + - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/iopath' + - run: + name: build + command: | + export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64 + python3 setup.py build_ext --inplace + - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64 python -m unittest discover -v -s tests -t . + - run: python3 setup.py bdist_wheel + + binary_linux_wheel: + <<: *binary_common + docker: + - image: << parameters.wheel_docker_image >> + auth: + username: $DOCKERHUB_USERNAME + password: $DOCKERHUB_TOKEN + resource_class: 2xlarge+ + steps: + - checkout + - run: MAX_JOBS=15 packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "<< parameters.conda_docker_image >>" + auth: + username: $DOCKERHUB_USERNAME + password: $DOCKERHUB_TOKEN + resource_class: 2xlarge+ + steps: + - checkout + # This is building with cuda but no gpu present, + # so we aren't running the tests. + - run: + name: build + no_output_timeout: 40m + command: MAX_JOBS=15 TEST_FLAG=--no-test python3 packaging/build_conda.py + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_linux_conda_cuda: + <<: *binary_common + machine: + image: linux-cuda-11:default + resource_class: gpu.nvidia.small.multi + steps: + - checkout + + - run: + name: Pull docker image + command: | + nvidia-smi + set -e + + { docker login -u="$DOCKERHUB_USERNAME" -p="$DOCKERHUB_TOKEN" ; } 2> /dev/null + + echo Pulling docker image $TESTRUN_DOCKER_IMAGE + docker pull $TESTRUN_DOCKER_IMAGE + - run: + name: Build and run tests + no_output_timeout: 40m + command: | + set -e + + cd ${HOME}/project/ + + export JUST_TESTRUN=1 + VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION -e JUST_TESTRUN" + + docker run --gpus all --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${TESTRUN_DOCKER_IMAGE} python3 ./packaging/build_conda.py + +workflows: + version: 2 + build_and_test: + jobs: + # - main: + # context: DOCKERHUB_TOKEN + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda117 + context: DOCKERHUB_TOKEN + cu_version: cu117 + name: linux_conda_py38_cu117_pyt200 + python_version: '3.8' + pytorch_version: 2.0.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt200 + python_version: '3.8' + pytorch_version: 2.0.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda117 + context: DOCKERHUB_TOKEN + cu_version: cu117 + name: linux_conda_py38_cu117_pyt201 + python_version: '3.8' + pytorch_version: 2.0.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt201 + python_version: '3.8' + pytorch_version: 2.0.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt210 + python_version: '3.8' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py38_cu121_pyt210 + python_version: '3.8' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt211 + python_version: '3.8' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py38_cu121_pyt211 + python_version: '3.8' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt212 + python_version: '3.8' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py38_cu121_pyt212 + python_version: '3.8' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt220 + python_version: '3.8' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py38_cu121_pyt220 + python_version: '3.8' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt222 + python_version: '3.8' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py38_cu121_pyt222 + python_version: '3.8' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py38_cu118_pyt231 + python_version: '3.8' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py38_cu121_pyt231 + python_version: '3.8' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda117 + context: DOCKERHUB_TOKEN + cu_version: cu117 + name: linux_conda_py39_cu117_pyt200 + python_version: '3.9' + pytorch_version: 2.0.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt200 + python_version: '3.9' + pytorch_version: 2.0.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda117 + context: DOCKERHUB_TOKEN + cu_version: cu117 + name: linux_conda_py39_cu117_pyt201 + python_version: '3.9' + pytorch_version: 2.0.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt201 + python_version: '3.9' + pytorch_version: 2.0.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt210 + python_version: '3.9' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py39_cu121_pyt210 + python_version: '3.9' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt211 + python_version: '3.9' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py39_cu121_pyt211 + python_version: '3.9' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt212 + python_version: '3.9' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py39_cu121_pyt212 + python_version: '3.9' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt220 + python_version: '3.9' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py39_cu121_pyt220 + python_version: '3.9' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt222 + python_version: '3.9' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py39_cu121_pyt222 + python_version: '3.9' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py39_cu118_pyt231 + python_version: '3.9' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py39_cu121_pyt231 + python_version: '3.9' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda117 + context: DOCKERHUB_TOKEN + cu_version: cu117 + name: linux_conda_py310_cu117_pyt200 + python_version: '3.10' + pytorch_version: 2.0.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt200 + python_version: '3.10' + pytorch_version: 2.0.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda117 + context: DOCKERHUB_TOKEN + cu_version: cu117 + name: linux_conda_py310_cu117_pyt201 + python_version: '3.10' + pytorch_version: 2.0.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt201 + python_version: '3.10' + pytorch_version: 2.0.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt210 + python_version: '3.10' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py310_cu121_pyt210 + python_version: '3.10' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt211 + python_version: '3.10' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py310_cu121_pyt211 + python_version: '3.10' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt212 + python_version: '3.10' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py310_cu121_pyt212 + python_version: '3.10' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt220 + python_version: '3.10' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py310_cu121_pyt220 + python_version: '3.10' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt222 + python_version: '3.10' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py310_cu121_pyt222 + python_version: '3.10' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py310_cu118_pyt231 + python_version: '3.10' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py310_cu121_pyt231 + python_version: '3.10' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py311_cu118_pyt210 + python_version: '3.11' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py311_cu121_pyt210 + python_version: '3.11' + pytorch_version: 2.1.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py311_cu118_pyt211 + python_version: '3.11' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py311_cu121_pyt211 + python_version: '3.11' + pytorch_version: 2.1.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py311_cu118_pyt212 + python_version: '3.11' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py311_cu121_pyt212 + python_version: '3.11' + pytorch_version: 2.1.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py311_cu118_pyt220 + python_version: '3.11' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py311_cu121_pyt220 + python_version: '3.11' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py311_cu118_pyt222 + python_version: '3.11' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py311_cu121_pyt222 + python_version: '3.11' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py311_cu118_pyt231 + python_version: '3.11' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py311_cu121_pyt231 + python_version: '3.11' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py312_cu118_pyt220 + python_version: '3.12' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py312_cu121_pyt220 + python_version: '3.12' + pytorch_version: 2.2.0 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py312_cu118_pyt222 + python_version: '3.12' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py312_cu121_pyt222 + python_version: '3.12' + pytorch_version: 2.2.2 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda118 + context: DOCKERHUB_TOKEN + cu_version: cu118 + name: linux_conda_py312_cu118_pyt231 + python_version: '3.12' + pytorch_version: 2.3.1 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda121 + context: DOCKERHUB_TOKEN + cu_version: cu121 + name: linux_conda_py312_cu121_pyt231 + python_version: '3.12' + pytorch_version: 2.3.1 + - binary_linux_conda_cuda: + name: testrun_conda_cuda_py310_cu117_pyt201 + context: DOCKERHUB_TOKEN + python_version: "3.10" + pytorch_version: '2.0.1' + cu_version: "cu117" diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.circleci/regenerate.py b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/regenerate.py new file mode 100644 index 0000000000000000000000000000000000000000..5e7d6257ac359d1967e02eb156c8939300cab1d8 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.circleci/regenerate.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This script is adapted from the torchvision one. +""" + +import os.path + +import jinja2 +import yaml +from packaging import version + + +# The CUDA versions which have pytorch conda packages available for linux for each +# version of pytorch. +CONDA_CUDA_VERSIONS = { + "2.0.0": ["cu117", "cu118"], + "2.0.1": ["cu117", "cu118"], + "2.1.0": ["cu118", "cu121"], + "2.1.1": ["cu118", "cu121"], + "2.1.2": ["cu118", "cu121"], + "2.2.0": ["cu118", "cu121"], + "2.2.2": ["cu118", "cu121"], + "2.3.1": ["cu118", "cu121"], +} + + +def conda_docker_image_for_cuda(cuda_version): + if len(cuda_version) != 5: + raise ValueError("Unknown cuda version") + return "pytorch/conda-builder:cuda" + cuda_version[2:] + + +def pytorch_versions_for_python(python_version): + if python_version in ["3.8", "3.9"]: + return list(CONDA_CUDA_VERSIONS) + if python_version == "3.10": + return [ + i + for i in CONDA_CUDA_VERSIONS + if version.Version(i) >= version.Version("1.11.0") + ] + if python_version == "3.11": + return [ + i + for i in CONDA_CUDA_VERSIONS + if version.Version(i) >= version.Version("2.1.0") + ] + if python_version == "3.12": + return [ + i + for i in CONDA_CUDA_VERSIONS + if version.Version(i) >= version.Version("2.2.0") + ] + + +def workflows(prefix="", filter_branch=None, upload=False, indentation=6): + w = [] + for btype in ["conda"]: + for python_version in ["3.8", "3.9", "3.10", "3.11", "3.12"]: + for pytorch_version in pytorch_versions_for_python(python_version): + for cu_version in CONDA_CUDA_VERSIONS[pytorch_version]: + w += workflow_pair( + btype=btype, + python_version=python_version, + pytorch_version=pytorch_version, + cu_version=cu_version, + prefix=prefix, + upload=upload, + filter_branch=filter_branch, + ) + + return indent(indentation, w) + + +def workflow_pair( + *, + btype, + python_version, + pytorch_version, + cu_version, + prefix="", + upload=False, + filter_branch, +): + + w = [] + py = python_version.replace(".", "") + pyt = pytorch_version.replace(".", "") + base_workflow_name = f"{prefix}linux_{btype}_py{py}_{cu_version}_pyt{pyt}" + + w.append( + generate_base_workflow( + base_workflow_name=base_workflow_name, + python_version=python_version, + pytorch_version=pytorch_version, + cu_version=cu_version, + btype=btype, + filter_branch=filter_branch, + ) + ) + + if upload: + w.append( + generate_upload_workflow( + base_workflow_name=base_workflow_name, + btype=btype, + cu_version=cu_version, + filter_branch=filter_branch, + ) + ) + + return w + + +def generate_base_workflow( + *, + base_workflow_name, + python_version, + cu_version, + pytorch_version, + btype, + filter_branch=None, +): + + d = { + "name": base_workflow_name, + "python_version": python_version, + "cu_version": cu_version, + "pytorch_version": pytorch_version, + "context": "DOCKERHUB_TOKEN", + } + + conda_docker_image = conda_docker_image_for_cuda(cu_version) + if conda_docker_image is not None: + d["conda_docker_image"] = conda_docker_image + + if filter_branch is not None: + d["filters"] = {"branches": {"only": filter_branch}} + + return {f"binary_linux_{btype}": d} + + +def generate_upload_workflow(*, base_workflow_name, btype, cu_version, filter_branch): + d = { + "name": f"{base_workflow_name}_upload", + "context": "org-member", + "requires": [base_workflow_name], + } + + if btype == "wheel": + d["subfolder"] = cu_version + "/" + + if filter_branch is not None: + d["filters"] = {"branches": {"only": filter_branch}} + + return {f"binary_{btype}_upload": d} + + +def indent(indentation, data_list): + if len(data_list) == 0: + return "" + return ("\n" + " " * indentation).join( + yaml.dump(data_list, default_flow_style=False).splitlines() + ) + + +if __name__ == "__main__": + d = os.path.dirname(__file__) + env = jinja2.Environment( + loader=jinja2.FileSystemLoader(d), + lstrip_blocks=True, + autoescape=False, + keep_trailing_newline=True, + ) + + with open(os.path.join(d, "config.yml"), "w") as f: + f.write(env.get_template("config.in.yml").render(workflows=workflows)) diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/CODE_OF_CONDUCT.md b/third_party/AnyBimanual/third_party/pytorch3d/.github/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..f049d4c53173cc44e0d0755b874d108891a5bfc5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.github/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/CONTRIBUTING.md b/third_party/AnyBimanual/third_party/pytorch3d/.github/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..b43d2141dcb817182e060190e18fb62cebf19e29 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.github/CONTRIBUTING.md @@ -0,0 +1,53 @@ +# Contributing to PyTorch3D +We want to make contributing to this project as easy and transparent as +possible. + +## Pull Requests +We actively welcome your pull requests. + +However, if you're adding any significant features, please make sure to have a corresponding issue to outline your proposal and motivation and allow time for us to give feedback, *before* you send a PR. +We do not always accept new features, and we take the following factors into consideration: + +- Whether the same feature can be achieved without modifying PyTorch3D directly. If any aspect of the API is not extensible, please highlight this in an issue so we can work on making this more extensible. +- Whether the feature is potentially useful to a large audience, or only to a small portion of users. +- Whether the proposed solution has a good design and interface. +- Whether the proposed solution adds extra mental/practical overhead to users who don't need such feature. +- Whether the proposed solution breaks existing APIs. + +When sending a PR, please ensure you complete the following steps: + +1. Fork the repo and create your branch from `main`. Follow the instructions + in [INSTALL.md](../INSTALL.md) to build the repo. +2. If you've added code that should be tested, add tests. +3. If you've changed any APIs, please update the documentation. +4. Ensure the test suite passes, by running this from the project root: + ``` + python -m unittest discover -v -s tests -t . + ``` +5. Make sure your code lints by running `dev/linter.sh` from the project root. +6. If a PR contains multiple orthogonal changes, split it into multiple separate PRs. +7. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe +disclosure of security bugs. In those cases, please go through the process +outlined on that page and do not file a public issue. + +## Coding Style +We follow these [python](http://google.github.io/styleguide/pyguide.html) and [C++](https://google.github.io/styleguide/cppguide.html) style guides. + +For the linter to work, you will need to install `black`, `flake`, `usort` and `clang-format`, and +they need to be fairly up to date. + +## License +By contributing to PyTorch3D, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/bugs.md b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/bugs.md new file mode 100644 index 0000000000000000000000000000000000000000..bbba16affdb8efa3dee61ede84e5d406350a613a --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/bugs.md @@ -0,0 +1,30 @@ +--- +name: "🐛 Bugs / Unexpected behaviors" +about: Please report unexpected behaviors or bugs in PyTorch3D. + +--- + +If you do not know the root cause of the problem / bug, and wish someone to help you, please +post according to this template: + +## 🐛 Bugs / Unexpected behaviors + + +NOTE: Please look at the existing list of Issues tagged with the label ['bug`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3Abug). **Only open a new issue if this bug has not already been reported. If an issue already exists, please comment there instead.**. + +## Instructions To Reproduce the Issue: + +Please include the following (depending on what the issue is): + +1. Any changes you made (`git diff`) or code you wrote +``` + +``` +2. The exact command(s) you ran: +3. What you observed (including the full logs): +``` + +``` + +Please also simplify the steps as much as possible so they do not require additional resources to + run, such as a private dataset. diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/config.yml b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..3ba13e0cec6cbbfd462e9ebf529dd2093148cd69 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1 @@ +blank_issues_enabled: false diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/feature_request.md b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000000000000000000000000000000000000..6eaf025dfdb1bdd0944c65f1ab25b34f438a05de --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,21 @@ +--- +name: "\U0001F680 Feature Request" +about: Submit a proposal/request for a new PyTorch3D feature + +--- + +## 🚀 Feature + + +NOTE: Please look at the existing list of Issues tagged with the label ['enhancement`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3Aenhancement). **Only open a new issue if you do not see your feature request there**. + +## Motivation + + + +## Pitch + + + +NOTE: we only consider adding new features if they are useful for many users. diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/questions-help.md b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/questions-help.md new file mode 100644 index 0000000000000000000000000000000000000000..639d0ff823abd7555d987fd586797b457ff43f3b --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/.github/ISSUE_TEMPLATE/questions-help.md @@ -0,0 +1,21 @@ +--- +name: "❓ Questions" +about: How do I do X with PyTorch3D? How does PyTorch3D do X? + +--- + +## ❓ Questions on how to use PyTorch3D + + + + +NOTE: Please look at the existing list of Issues tagged with the label ['question`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3Aquestion) or ['how-to`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3A%22how+to%22). **Only open a new issue if you cannot find an answer there**. + +Also note the following: + +1. If you encountered any errors or unexpected issues while using PyTorch3D and need help resolving them, + please use the "Bugs / Unexpected behaviors" issue template. + +2. We do not answer general machine learning / computer vision questions that are not specific to + PyTorch3D, such as how a model works or what algorithm/methods can be + used to achieve X. diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/pointcloud_render.png b/third_party/AnyBimanual/third_party/pytorch3d/.github/pointcloud_render.png new file mode 100644 index 0000000000000000000000000000000000000000..243f2e07ca3dde89e8ba2385145f86ddd496fe1d Binary files /dev/null and b/third_party/AnyBimanual/third_party/pytorch3d/.github/pointcloud_render.png differ diff --git a/third_party/AnyBimanual/third_party/pytorch3d/.github/pytorch3dlogo.png b/third_party/AnyBimanual/third_party/pytorch3d/.github/pytorch3dlogo.png new file mode 100644 index 0000000000000000000000000000000000000000..d8e435cc83a1b16ffef65a47107d3922bbcbb101 Binary files /dev/null and b/third_party/AnyBimanual/third_party/pytorch3d/.github/pytorch3dlogo.png differ diff --git a/third_party/AnyBimanual/third_party/pytorch3d/dev/linter.sh b/third_party/AnyBimanual/third_party/pytorch3d/dev/linter.sh new file mode 100644 index 0000000000000000000000000000000000000000..43c64084d31229ba18560997735637030cdd2a2f --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/dev/linter.sh @@ -0,0 +1,40 @@ +#!/bin/bash -e +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# Run this script at project root by "./dev/linter.sh" before you commit + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +DIR=$(dirname "${DIR}") + +if [[ -f "${DIR}/TARGETS" ]] +then + pyfmt "${DIR}" +else +# run usort externally only + echo "Running usort..." + usort "${DIR}" +fi + +echo "Running black..." +black "${DIR}" + +echo "Running flake..." +flake8 "${DIR}" || true + +echo "Running clang-format ..." +clangformat=$(command -v clang-format-8 || echo clang-format) +find "${DIR}" -regex ".*\.\(cpp\|c\|cc\|cu\|cuh\|cxx\|h\|hh\|hpp\|hxx\|tcc\|mm\|m\)" -print0 | xargs -0 "${clangformat}" -i + +# Run arc and pyre internally only. +if [[ -f "${DIR}/TARGETS" ]] +then + (cd "${DIR}"; command -v arc > /dev/null && arc lint) || true + + echo "Running pyre..." + echo "To restart/kill pyre server, run 'pyre restart' or 'pyre kill' in fbcode/" + ( cd ~/fbsource/fbcode; pyre -l vision/fair/pytorch3d/ ) +fi diff --git a/third_party/AnyBimanual/third_party/pytorch3d/dev/run_tutorials.sh b/third_party/AnyBimanual/third_party/pytorch3d/dev/run_tutorials.sh new file mode 100644 index 0000000000000000000000000000000000000000..7517ea8c74131ab7d6be3c7bf074034a601b9fb3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/dev/run_tutorials.sh @@ -0,0 +1,56 @@ +#!/usr/bin/bash +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# This script is for running some of the tutorials using the nightly build in +# an isolated environment. It is designed to be run in docker. + +# If you run this script in this directory with +# sudo docker run --runtime=nvidia -it --rm -v $PWD/../docs/tutorials:/notebooks -v $PWD:/loc pytorch/conda-cuda bash /loc/run_tutorials.sh | tee log.txt +# it should execute some tutorials with the nightly build and resave them, and +# save a log in the current directory. + +# We use nbconvert. runipy would be an alternative but it currently doesn't +# work well with plotly. + +set -e + +conda init bash +# shellcheck source=/dev/null +source ~/.bashrc +conda create -y -n myenv python=3.8 matplotlib ipython ipywidgets nbconvert +conda activate myenv +conda install -y -c iopath iopath +conda install -y -c pytorch pytorch=1.6.0 cudatoolkit=10.1 torchvision +conda install -y -c pytorch3d-nightly pytorch3d +pip install plotly scikit-image + +for notebook in /notebooks/*.ipynb +do + name=$(basename "$notebook") + + if [[ "$name" == "dataloaders_ShapeNetCore_R2N2.ipynb" ]] + then + #skip as data not easily available + continue + fi + if [[ "$name" == "render_densepose.ipynb" ]] + then + #skip as data not easily available + continue + fi + + #comment the lines which install torch, torchvision and pytorch3d + sed -Ei '/(torchvision)|(pytorch3d)/ s/!pip/!#pip/' "$notebook" + #Don't let tqdm use widgets + sed -i 's/from tqdm.notebook import tqdm/from tqdm import tqdm/' "$notebook" + + echo + echo "### ### ###" + echo "starting $name" + time jupyter nbconvert --to notebook --inplace --ExecutePreprocessor.kernel_name=python3 --execute "$notebook" || true + echo "ending $name" +done diff --git a/third_party/AnyBimanual/third_party/pytorch3d/dev/test_list.py b/third_party/AnyBimanual/third_party/pytorch3d/dev/test_list.py new file mode 100644 index 0000000000000000000000000000000000000000..f00facd8ddae2414c431cbae9ba5f9a810a0e15a --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/dev/test_list.py @@ -0,0 +1,65 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import ast +from pathlib import Path +from typing import List + + +""" +This module outputs a list of tests for completion. +It has no dependencies. +""" + + +def get_test_files() -> List[Path]: + root = Path(__file__).parent.parent + dirs = ["tests", "projects/implicitron_trainer"] + return [i for dir in dirs for i in (root / dir).glob("**/test*.py")] + + +def tests_from_file(path: Path, base: str) -> List[str]: + """ + Returns all the tests in the given file, in format + expected as arguments when running the tests. + e.g. + file_stem + file_stem.TestFunctionality + file_stem.TestFunctionality.test_f + file_stem.TestFunctionality.test_g + """ + with open(path) as f: + node = ast.parse(f.read()) + out = [base] + for cls in node.body: + if not isinstance(cls, ast.ClassDef): + continue + if not cls.name.startswith("Test"): + continue + class_base = base + "." + cls.name + out.append(class_base) + for method in cls.body: + if not isinstance(method, ast.FunctionDef): + continue + if not method.name.startswith("test"): + continue + out.append(class_base + "." + method.name) + return out + + +def main() -> None: + files = get_test_files() + test_root = Path(__file__).parent.parent + all_tests = [] + for f in files: + file_base = str(f.relative_to(test_root))[:-3].replace("/", ".") + all_tests.extend(tests_from_file(f, file_base)) + for test in sorted(all_tests): + print(test) + + +if __name__ == "__main__": + main() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_basic.py b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_basic.py new file mode 100644 index 0000000000000000000000000000000000000000..be547e340ec4f7fe1b9348d022d36673fe575bd8 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_basic.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This example demonstrates the most trivial, direct interface of the pulsar +sphere renderer. It renders and saves an image with 10 random spheres. +Output: basic.png. +""" +import logging +import math +from os import path + +import imageio +import torch +from pytorch3d.renderer.points.pulsar import Renderer + + +LOGGER = logging.getLogger(__name__) + + +def cli(): + """ + Basic example for the pulsar sphere renderer. + + Writes to `basic.png`. + """ + LOGGER.info("Rendering on GPU...") + torch.manual_seed(1) + n_points = 10 + width = 1_000 + height = 1_000 + device = torch.device("cuda") + # The PyTorch3D system is right handed; in pulsar you can choose the handedness. + # For easy reproducibility we use a right handed coordinate system here. + renderer = Renderer(width, height, n_points, right_handed_system=True).to(device) + # Generate sample data. + vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 10.0 + vert_pos[:, 2] += 25.0 + vert_pos[:, :2] -= 5.0 + vert_col = torch.rand(n_points, 3, dtype=torch.float32, device=device) + vert_rad = torch.rand(n_points, dtype=torch.float32, device=device) + cam_params = torch.tensor( + [ + 0.0, + 0.0, + 0.0, # Position 0, 0, 0 (x, y, z). + 0.0, + math.pi, # Because of the right handed system, the camera must look 'back'. + 0.0, # Rotation 0, 0, 0 (in axis-angle format). + 5.0, # Focal length in world size. + 2.0, # Sensor size in world size. + ], + dtype=torch.float32, + device=device, + ) + # Render. + image = renderer( + vert_pos, + vert_col, + vert_rad, + cam_params, + 1.0e-1, # Renderer blending parameter gamma, in [1., 1e-5]. + 45.0, # Maximum depth. + ) + LOGGER.info("Writing image to `%s`.", path.abspath("basic.png")) + imageio.imsave("basic.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy()) + LOGGER.info("Done.") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + cli() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_basic_unified.py b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_basic_unified.py new file mode 100644 index 0000000000000000000000000000000000000000..ae93f49d928adb46eda2857f3cf919a4f364882e --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_basic_unified.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This example demonstrates the most trivial use of the pulsar PyTorch3D +interface for sphere renderering. It renders and saves an image with +10 random spheres. +Output: basic-pt3d.png. +""" +import logging +from os import path + +import imageio +import torch +from pytorch3d.renderer import ( + PerspectiveCameras, + PointsRasterizationSettings, + PointsRasterizer, + PulsarPointsRenderer, +) +from pytorch3d.structures import Pointclouds + + +LOGGER = logging.getLogger(__name__) + + +def cli(): + """ + Basic example for the pulsar sphere renderer using the PyTorch3D interface. + + Writes to `basic-pt3d.png`. + """ + LOGGER.info("Rendering on GPU...") + torch.manual_seed(1) + n_points = 10 + width = 1_000 + height = 1_000 + device = torch.device("cuda") + # Generate sample data. + vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 10.0 + vert_pos[:, 2] += 25.0 + vert_pos[:, :2] -= 5.0 + vert_col = torch.rand(n_points, 3, dtype=torch.float32, device=device) + pcl = Pointclouds(points=vert_pos[None, ...], features=vert_col[None, ...]) + # Alternatively, you can also use the look_at_view_transform to get R and T: + # R, T = look_at_view_transform( + # dist=30.0, elev=0.0, azim=180.0, at=((0.0, 0.0, 30.0),), up=((0, 1, 0),), + # ) + cameras = PerspectiveCameras( + # The focal length must be double the size for PyTorch3D because of the NDC + # coordinates spanning a range of two - and they must be normalized by the + # sensor width (see the pulsar example). This means we need here + # 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar. + focal_length=(5.0 * 2.0 / 2.0,), + R=torch.eye(3, dtype=torch.float32, device=device)[None, ...], + T=torch.zeros((1, 3), dtype=torch.float32, device=device), + image_size=((height, width),), + device=device, + ) + vert_rad = torch.rand(n_points, dtype=torch.float32, device=device) + raster_settings = PointsRasterizationSettings( + image_size=(height, width), + radius=vert_rad, + ) + rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings) + renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device) + # Render. + image = renderer( + pcl, + gamma=(1.0e-1,), # Renderer blending parameter gamma, in [1., 1e-5]. + znear=(1.0,), + zfar=(45.0,), + radius_world=True, + bg_col=torch.ones((3,), dtype=torch.float32, device=device), + )[0] + LOGGER.info("Writing image to `%s`.", path.abspath("basic-pt3d.png")) + imageio.imsave( + "basic-pt3d.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy() + ) + LOGGER.info("Done.") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + cli() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_cam.py b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_cam.py new file mode 100644 index 0000000000000000000000000000000000000000..cd1dfabc60a414d27a6743e77b1f984002edc46a --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_cam.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This example demonstrates camera parameter optimization with the plain +pulsar interface. For this, a reference image has been pre-generated +(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_cam.png`). +The same scene parameterization is loaded and the camera parameters +distorted. Gradient-based optimization is used to converge towards the +original camera parameters. +Output: cam.gif. +""" +import logging +import math +from os import path + +import cv2 +import imageio +import numpy as np +import torch +from pytorch3d.renderer.points.pulsar import Renderer +from pytorch3d.transforms import axis_angle_to_matrix, matrix_to_rotation_6d +from torch import nn, optim + + +LOGGER = logging.getLogger(__name__) +N_POINTS = 20 +WIDTH = 1_000 +HEIGHT = 1_000 +DEVICE = torch.device("cuda") + + +class SceneModel(nn.Module): + """ + A simple scene model to demonstrate use of pulsar in PyTorch modules. + + The scene model is parameterized with sphere locations (vert_pos), + channel content (vert_col), radiuses (vert_rad), camera position (cam_pos), + camera rotation (cam_rot) and sensor focal length and width (cam_sensor). + + The forward method of the model renders this scene description. Any + of these parameters could instead be passed as inputs to the forward + method and come from a different model. + """ + + def __init__(self): + super(SceneModel, self).__init__() + self.gamma = 0.1 + # Points. + torch.manual_seed(1) + vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0 + vert_pos[:, 2] += 25.0 + vert_pos[:, :2] -= 5.0 + self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False)) + self.register_parameter( + "vert_col", + nn.Parameter( + torch.rand(N_POINTS, 3, dtype=torch.float32), requires_grad=False + ), + ) + self.register_parameter( + "vert_rad", + nn.Parameter( + torch.rand(N_POINTS, dtype=torch.float32), requires_grad=False + ), + ) + self.register_parameter( + "cam_pos", + nn.Parameter( + torch.tensor([0.1, 0.1, 0.0], dtype=torch.float32), requires_grad=True + ), + ) + self.register_parameter( + "cam_rot", + # We're using the 6D rot. representation for better gradients. + nn.Parameter( + matrix_to_rotation_6d( + axis_angle_to_matrix( + torch.tensor( + [ + [0.02, math.pi + 0.02, 0.01], + ], + dtype=torch.float32, + ) + ) + )[0], + requires_grad=True, + ), + ) + self.register_parameter( + "cam_sensor", + nn.Parameter( + torch.tensor([4.8, 1.8], dtype=torch.float32), requires_grad=True + ), + ) + self.renderer = Renderer(WIDTH, HEIGHT, N_POINTS, right_handed_system=True) + + def forward(self): + return self.renderer.forward( + self.vert_pos, + self.vert_col, + self.vert_rad, + torch.cat([self.cam_pos, self.cam_rot, self.cam_sensor]), + self.gamma, + 45.0, + ) + + +def cli(): + """ + Camera optimization example using pulsar. + + Writes to `cam.gif`. + """ + LOGGER.info("Loading reference...") + # Load reference. + ref = ( + torch.from_numpy( + imageio.imread( + "../../tests/pulsar/reference/examples_TestRenderer_test_cam.png" + )[:, ::-1, :].copy() + ).to(torch.float32) + / 255.0 + ).to(DEVICE) + # Set up model. + model = SceneModel().to(DEVICE) + # Optimizer. + optimizer = optim.SGD( + [ + {"params": [model.cam_pos], "lr": 1e-4}, # 1e-3 + {"params": [model.cam_rot], "lr": 5e-6}, + {"params": [model.cam_sensor], "lr": 1e-4}, + ] + ) + + LOGGER.info("Writing video to `%s`.", path.abspath("cam.gif")) + writer = imageio.get_writer("cam.gif", format="gif", fps=25) + + # Optimize. + for i in range(300): + optimizer.zero_grad() + result = model() + # Visualize. + result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8) + cv2.imshow("opt", result_im[:, :, ::-1]) + writer.append_data(result_im) + overlay_img = np.ascontiguousarray( + ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[ + :, :, ::-1 + ] + ) + overlay_img = cv2.putText( + overlay_img, + "Step %d" % (i), + (10, 40), + cv2.FONT_HERSHEY_SIMPLEX, + 1, + (0, 0, 0), + 2, + cv2.LINE_AA, + False, + ) + cv2.imshow("overlay", overlay_img) + cv2.waitKey(1) + # Update. + loss = ((result - ref) ** 2).sum() + LOGGER.info("loss %d: %f", i, loss.item()) + loss.backward() + optimizer.step() + writer.close() + LOGGER.info("Done.") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + cli() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_cam_unified.py b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_cam_unified.py new file mode 100644 index 0000000000000000000000000000000000000000..8ef3a8df408a8750acc4fc415174f1456d25836b --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_cam_unified.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This example demonstrates camera parameter optimization with the pulsar +PyTorch3D interface. For this, a reference image has been pre-generated +(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_cam.png`). +The same scene parameterization is loaded and the camera parameters +distorted. Gradient-based optimization is used to converge towards the +original camera parameters. +Output: cam-pt3d.gif +""" +import logging +from os import path + +import cv2 +import imageio +import numpy as np +import torch +from pytorch3d.renderer.cameras import PerspectiveCameras +from pytorch3d.renderer.points import ( + PointsRasterizationSettings, + PointsRasterizer, + PulsarPointsRenderer, +) +from pytorch3d.structures.pointclouds import Pointclouds +from pytorch3d.transforms import axis_angle_to_matrix +from torch import nn, optim + + +LOGGER = logging.getLogger(__name__) +N_POINTS = 20 +WIDTH = 1_000 +HEIGHT = 1_000 +DEVICE = torch.device("cuda") + + +class SceneModel(nn.Module): + """ + A simple scene model to demonstrate use of pulsar in PyTorch modules. + + The scene model is parameterized with sphere locations (vert_pos), + channel content (vert_col), radiuses (vert_rad), camera position (cam_pos), + camera rotation (cam_rot) and sensor focal length and width (cam_sensor). + + The forward method of the model renders this scene description. Any + of these parameters could instead be passed as inputs to the forward + method and come from a different model. + """ + + def __init__(self): + super(SceneModel, self).__init__() + self.gamma = 0.1 + # Points. + torch.manual_seed(1) + vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0 + vert_pos[:, 2] += 25.0 + vert_pos[:, :2] -= 5.0 + self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False)) + self.register_parameter( + "vert_col", + nn.Parameter( + torch.rand(N_POINTS, 3, dtype=torch.float32), + requires_grad=False, + ), + ) + self.register_parameter( + "vert_rad", + nn.Parameter( + torch.rand(N_POINTS, dtype=torch.float32), + requires_grad=False, + ), + ) + self.register_parameter( + "cam_pos", + nn.Parameter( + torch.tensor([0.1, 0.1, 0.0], dtype=torch.float32), + requires_grad=True, + ), + ) + self.register_parameter( + "cam_rot", + # We're using the 6D rot. representation for better gradients. + nn.Parameter( + axis_angle_to_matrix( + torch.tensor( + [ + [0.02, 0.02, 0.01], + ], + dtype=torch.float32, + ) + )[0], + requires_grad=True, + ), + ) + self.register_parameter( + "focal_length", + nn.Parameter( + torch.tensor( + [ + 4.8 * 2.0 / 2.0, + ], + dtype=torch.float32, + ), + requires_grad=True, + ), + ) + self.cameras = PerspectiveCameras( + # The focal length must be double the size for PyTorch3D because of the NDC + # coordinates spanning a range of two - and they must be normalized by the + # sensor width (see the pulsar example). This means we need here + # 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar. + # + # R, T and f are provided here, but will be provided again + # at every call to the forward method. The reason are problems + # with PyTorch which makes device placement for gradients problematic + # for tensors which are themselves on a 'gradient path' but not + # leafs in the calculation tree. This will be addressed by an architectural + # change in PyTorch3D in the future. Until then, this workaround is + # recommended. + focal_length=self.focal_length, + R=self.cam_rot[None, ...], + T=self.cam_pos[None, ...], + image_size=((HEIGHT, WIDTH),), + device=DEVICE, + ) + raster_settings = PointsRasterizationSettings( + image_size=(HEIGHT, WIDTH), + radius=self.vert_rad, + ) + rasterizer = PointsRasterizer( + cameras=self.cameras, raster_settings=raster_settings + ) + self.renderer = PulsarPointsRenderer(rasterizer=rasterizer) + + def forward(self): + # The Pointclouds object creates copies of it's arguments - that's why + # we have to create a new object in every forward step. + pcl = Pointclouds( + points=self.vert_pos[None, ...], features=self.vert_col[None, ...] + ) + return self.renderer( + pcl, + gamma=(self.gamma,), + zfar=(45.0,), + znear=(1.0,), + radius_world=True, + bg_col=torch.ones((3,), dtype=torch.float32, device=DEVICE), + # As mentioned above: workaround for device placement of gradients for + # camera parameters. + focal_length=self.focal_length, + R=self.cam_rot[None, ...], + T=self.cam_pos[None, ...], + )[0] + + +def cli(): + """ + Camera optimization example using pulsar. + + Writes to `cam.gif`. + """ + LOGGER.info("Loading reference...") + # Load reference. + ref = ( + torch.from_numpy( + imageio.imread( + "../../tests/pulsar/reference/examples_TestRenderer_test_cam.png" + )[:, ::-1, :].copy() + ).to(torch.float32) + / 255.0 + ).to(DEVICE) + # Set up model. + model = SceneModel().to(DEVICE) + # Optimizer. + optimizer = optim.SGD( + [ + {"params": [model.cam_pos], "lr": 1e-4}, + {"params": [model.cam_rot], "lr": 5e-6}, + # Using a higher lr for the focal length here, because + # the sensor width can not be optimized directly. + {"params": [model.focal_length], "lr": 1e-3}, + ] + ) + + LOGGER.info("Writing video to `%s`.", path.abspath("cam-pt3d.gif")) + writer = imageio.get_writer("cam-pt3d.gif", format="gif", fps=25) + + # Optimize. + for i in range(300): + optimizer.zero_grad() + result = model() + # Visualize. + result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8) + cv2.imshow("opt", result_im[:, :, ::-1]) + writer.append_data(result_im) + overlay_img = np.ascontiguousarray( + ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[ + :, :, ::-1 + ] + ) + overlay_img = cv2.putText( + overlay_img, + "Step %d" % (i), + (10, 40), + cv2.FONT_HERSHEY_SIMPLEX, + 1, + (0, 0, 0), + 2, + cv2.LINE_AA, + False, + ) + cv2.imshow("overlay", overlay_img) + cv2.waitKey(1) + # Update. + loss = ((result - ref) ** 2).sum() + LOGGER.info("loss %d: %f", i, loss.item()) + loss.backward() + optimizer.step() + writer.close() + LOGGER.info("Done.") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + cli() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_multiview.py b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_multiview.py new file mode 100644 index 0000000000000000000000000000000000000000..a6a05b4a6ed895f1a2f465d073559b3dad73f6dd --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_multiview.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This example demonstrates multiview 3D reconstruction using the plain +pulsar interface. For this, reference images have been pre-generated +(you can find them at +`../../tests/pulsar/reference/examples_TestRenderer_test_multiview_%d.png`). +The camera parameters are assumed given. The scene is initialized with +random spheres. Gradient-based optimization is used to optimize sphere +parameters and prune spheres to converge to a 3D representation. + +This example is not available yet through the 'unified' interface, +because opacity support has not landed in PyTorch3D for general data +structures yet. +""" +import logging +import math +from os import path + +import cv2 +import imageio +import numpy as np +import torch +from pytorch3d.renderer.points.pulsar import Renderer +from torch import nn, optim + + +LOGGER = logging.getLogger(__name__) +N_POINTS = 400_000 +WIDTH = 1_000 +HEIGHT = 1_000 +VISUALIZE_IDS = [0, 1] +DEVICE = torch.device("cuda") + + +class SceneModel(nn.Module): + """ + A simple scene model to demonstrate use of pulsar in PyTorch modules. + + The scene model is parameterized with sphere locations (vert_pos), + channel content (vert_col), radiuses (vert_rad), camera position (cam_pos), + camera rotation (cam_rot) and sensor focal length and width (cam_sensor). + + The forward method of the model renders this scene description. Any + of these parameters could instead be passed as inputs to the forward + method and come from a different model. Optionally, camera parameters can + be provided to the forward method in which case the scene is rendered + using those parameters. + """ + + def __init__(self): + super(SceneModel, self).__init__() + self.gamma = 1.0 + # Points. + torch.manual_seed(1) + vert_pos = torch.rand((1, N_POINTS, 3), dtype=torch.float32) * 10.0 + vert_pos[:, :, 2] += 25.0 + vert_pos[:, :, :2] -= 5.0 + self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True)) + self.register_parameter( + "vert_col", + nn.Parameter( + torch.ones(1, N_POINTS, 3, dtype=torch.float32) * 0.5, + requires_grad=True, + ), + ) + self.register_parameter( + "vert_rad", + nn.Parameter( + torch.ones(1, N_POINTS, dtype=torch.float32) * 0.05, requires_grad=True + ), + ) + self.register_parameter( + "vert_opy", + nn.Parameter( + torch.ones(1, N_POINTS, dtype=torch.float32), requires_grad=True + ), + ) + self.register_buffer( + "cam_params", + torch.tensor( + [ + [ + np.sin(angle) * 35.0, + 0.0, + 30.0 - np.cos(angle) * 35.0, + 0.0, + -angle + math.pi, + 0.0, + 5.0, + 2.0, + ] + for angle in [-1.5, -0.8, -0.4, -0.1, 0.1, 0.4, 0.8, 1.5] + ], + dtype=torch.float32, + ), + ) + self.renderer = Renderer(WIDTH, HEIGHT, N_POINTS, right_handed_system=True) + + def forward(self, cam=None): + if cam is None: + cam = self.cam_params + n_views = 8 + else: + n_views = 1 + return self.renderer.forward( + self.vert_pos.expand(n_views, -1, -1), + self.vert_col.expand(n_views, -1, -1), + self.vert_rad.expand(n_views, -1), + cam, + self.gamma, + 45.0, + ) + + +def cli(): + """ + Simple demonstration for a multi-view 3D reconstruction using pulsar. + + This example makes use of opacity, which is not yet supported through + the unified PyTorch3D interface. + + Writes to `multiview.gif`. + """ + LOGGER.info("Loading reference...") + # Load reference. + ref = torch.stack( + [ + torch.from_numpy( + imageio.imread( + "../../tests/pulsar/reference/examples_TestRenderer_test_multiview_%d.png" + % idx + ) + ).to(torch.float32) + / 255.0 + for idx in range(8) + ] + ).to(DEVICE) + # Set up model. + model = SceneModel().to(DEVICE) + # Optimizer. + optimizer = optim.SGD( + [ + {"params": [model.vert_col], "lr": 1e-1}, + {"params": [model.vert_rad], "lr": 1e-3}, + {"params": [model.vert_pos], "lr": 1e-3}, + ] + ) + + # For visualization. + angle = 0.0 + LOGGER.info("Writing video to `%s`.", path.abspath("multiview.avi")) + writer = imageio.get_writer("multiview.gif", format="gif", fps=25) + + # Optimize. + for i in range(300): + optimizer.zero_grad() + result = model() + # Visualize. + result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8) + cv2.imshow("opt", result_im[0, :, :, ::-1]) + overlay_img = np.ascontiguousarray( + ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[ + 0, :, :, ::-1 + ] + ) + overlay_img = cv2.putText( + overlay_img, + "Step %d" % (i), + (10, 40), + cv2.FONT_HERSHEY_SIMPLEX, + 1, + (0, 0, 0), + 2, + cv2.LINE_AA, + False, + ) + cv2.imshow("overlay", overlay_img) + cv2.waitKey(1) + # Update. + loss = ((result - ref) ** 2).sum() + LOGGER.info("loss %d: %f", i, loss.item()) + loss.backward() + optimizer.step() + # Cleanup. + with torch.no_grad(): + model.vert_col.data = torch.clamp(model.vert_col.data, 0.0, 1.0) + # Remove points. + model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0 + model.vert_rad.data[model.vert_rad < 0.001] = 0.0001 + vd = ( + (model.vert_col - torch.ones(1, 1, 3, dtype=torch.float32).to(DEVICE)) + .abs() + .sum(dim=2) + ) + model.vert_pos.data[vd <= 0.2] = -1000.0 + # Rotating visualization. + cam_control = torch.tensor( + [ + [ + np.sin(angle) * 35.0, + 0.0, + 30.0 - np.cos(angle) * 35.0, + 0.0, + -angle + math.pi, + 0.0, + 5.0, + 2.0, + ] + ], + dtype=torch.float32, + ).to(DEVICE) + with torch.no_grad(): + result = model.forward(cam=cam_control)[0] + result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8) + cv2.imshow("vis", result_im[:, :, ::-1]) + writer.append_data(result_im) + angle += 0.05 + writer.close() + LOGGER.info("Done.") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + cli() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_optimization.py b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..530233f3f11f47ad78fb9a04bbc22004ac8c620f --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_optimization.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This example demonstrates scene optimization with the plain +pulsar interface. For this, a reference image has been pre-generated +(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png`). +The scene is initialized with random spheres. Gradient-based +optimization is used to converge towards a faithful +scene representation. +""" +import logging +import math + +import cv2 +import imageio +import numpy as np +import torch +from pytorch3d.renderer.points.pulsar import Renderer +from torch import nn, optim + + +LOGGER = logging.getLogger(__name__) +N_POINTS = 10_000 +WIDTH = 1_000 +HEIGHT = 1_000 +DEVICE = torch.device("cuda") + + +class SceneModel(nn.Module): + """ + A simple scene model to demonstrate use of pulsar in PyTorch modules. + + The scene model is parameterized with sphere locations (vert_pos), + channel content (vert_col), radiuses (vert_rad), camera position (cam_pos), + camera rotation (cam_rot) and sensor focal length and width (cam_sensor). + + The forward method of the model renders this scene description. Any + of these parameters could instead be passed as inputs to the forward + method and come from a different model. + """ + + def __init__(self): + super(SceneModel, self).__init__() + self.gamma = 1.0 + # Points. + torch.manual_seed(1) + vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0 + vert_pos[:, 2] += 25.0 + vert_pos[:, :2] -= 5.0 + self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True)) + self.register_parameter( + "vert_col", + nn.Parameter( + torch.ones(N_POINTS, 3, dtype=torch.float32) * 0.5, requires_grad=True + ), + ) + self.register_parameter( + "vert_rad", + nn.Parameter( + torch.ones(N_POINTS, dtype=torch.float32) * 0.3, requires_grad=True + ), + ) + self.register_buffer( + "cam_params", + torch.tensor( + [0.0, 0.0, 0.0, 0.0, math.pi, 0.0, 5.0, 2.0], dtype=torch.float32 + ), + ) + # The volumetric optimization works better with a higher number of tracked + # intersections per ray. + self.renderer = Renderer( + WIDTH, HEIGHT, N_POINTS, n_track=32, right_handed_system=True + ) + + def forward(self): + return self.renderer.forward( + self.vert_pos, + self.vert_col, + self.vert_rad, + self.cam_params, + self.gamma, + 45.0, + return_forward_info=True, + ) + + +def cli(): + """ + Scene optimization example using pulsar. + """ + LOGGER.info("Loading reference...") + # Load reference. + ref = ( + torch.from_numpy( + imageio.imread( + "../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png" + )[:, ::-1, :].copy() + ).to(torch.float32) + / 255.0 + ).to(DEVICE) + # Set up model. + model = SceneModel().to(DEVICE) + # Optimizer. + optimizer = optim.SGD( + [ + {"params": [model.vert_col], "lr": 1e0}, + {"params": [model.vert_rad], "lr": 5e-3}, + {"params": [model.vert_pos], "lr": 1e-2}, + ] + ) + LOGGER.info("Optimizing...") + # Optimize. + for i in range(500): + optimizer.zero_grad() + result, result_info = model() + # Visualize. + result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8) + cv2.imshow("opt", result_im[:, :, ::-1]) + overlay_img = np.ascontiguousarray( + ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[ + :, :, ::-1 + ] + ) + overlay_img = cv2.putText( + overlay_img, + "Step %d" % (i), + (10, 40), + cv2.FONT_HERSHEY_SIMPLEX, + 1, + (0, 0, 0), + 2, + cv2.LINE_AA, + False, + ) + cv2.imshow("overlay", overlay_img) + cv2.waitKey(1) + # Update. + loss = ((result - ref) ** 2).sum() + LOGGER.info("loss %d: %f", i, loss.item()) + loss.backward() + optimizer.step() + # Cleanup. + with torch.no_grad(): + model.vert_col.data = torch.clamp(model.vert_col.data, 0.0, 1.0) + # Remove points. + model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0 + model.vert_rad.data[model.vert_rad < 0.001] = 0.0001 + vd = ( + (model.vert_col - torch.ones(3, dtype=torch.float32).to(DEVICE)) + .abs() + .sum(dim=1) + ) + model.vert_pos.data[vd <= 0.2] = -1000.0 + LOGGER.info("Done.") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + cli() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_optimization_unified.py b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_optimization_unified.py new file mode 100644 index 0000000000000000000000000000000000000000..be4d92998231bd3aeb7c0e3bfc1ad08d97d8bca9 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/examples/pulsar_optimization_unified.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +This example demonstrates scene optimization with the PyTorch3D +pulsar interface. For this, a reference image has been pre-generated +(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png`). +The scene is initialized with random spheres. Gradient-based +optimization is used to converge towards a faithful +scene representation. +""" +import logging +import math + +import cv2 +import imageio +import numpy as np +import torch +from pytorch3d.renderer.cameras import PerspectiveCameras +from pytorch3d.renderer.points import ( + PointsRasterizationSettings, + PointsRasterizer, + PulsarPointsRenderer, +) +from pytorch3d.structures.pointclouds import Pointclouds +from torch import nn, optim + + +LOGGER = logging.getLogger(__name__) +N_POINTS = 10_000 +WIDTH = 1_000 +HEIGHT = 1_000 +DEVICE = torch.device("cuda") + + +class SceneModel(nn.Module): + """ + A simple scene model to demonstrate use of pulsar in PyTorch modules. + + The scene model is parameterized with sphere locations (vert_pos), + channel content (vert_col), radiuses (vert_rad), camera position (cam_pos), + camera rotation (cam_rot) and sensor focal length and width (cam_sensor). + + The forward method of the model renders this scene description. Any + of these parameters could instead be passed as inputs to the forward + method and come from a different model. + """ + + def __init__(self): + super(SceneModel, self).__init__() + self.gamma = 1.0 + # Points. + torch.manual_seed(1) + vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32, device=DEVICE) * 10.0 + vert_pos[:, 2] += 25.0 + vert_pos[:, :2] -= 5.0 + self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True)) + self.register_parameter( + "vert_col", + nn.Parameter( + torch.ones(N_POINTS, 3, dtype=torch.float32, device=DEVICE) * 0.5, + requires_grad=True, + ), + ) + self.register_parameter( + "vert_rad", + nn.Parameter( + torch.ones(N_POINTS, dtype=torch.float32) * 0.3, requires_grad=True + ), + ) + self.register_buffer( + "cam_params", + torch.tensor( + [0.0, 0.0, 0.0, 0.0, math.pi, 0.0, 5.0, 2.0], dtype=torch.float32 + ), + ) + self.cameras = PerspectiveCameras( + # The focal length must be double the size for PyTorch3D because of the NDC + # coordinates spanning a range of two - and they must be normalized by the + # sensor width (see the pulsar example). This means we need here + # 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar. + focal_length=5.0, + R=torch.eye(3, dtype=torch.float32, device=DEVICE)[None, ...], + T=torch.zeros((1, 3), dtype=torch.float32, device=DEVICE), + image_size=((HEIGHT, WIDTH),), + device=DEVICE, + ) + raster_settings = PointsRasterizationSettings( + image_size=(HEIGHT, WIDTH), + radius=self.vert_rad, + ) + rasterizer = PointsRasterizer( + cameras=self.cameras, raster_settings=raster_settings + ) + self.renderer = PulsarPointsRenderer(rasterizer=rasterizer, n_track=32) + + def forward(self): + # The Pointclouds object creates copies of it's arguments - that's why + # we have to create a new object in every forward step. + pcl = Pointclouds( + points=self.vert_pos[None, ...], features=self.vert_col[None, ...] + ) + return self.renderer( + pcl, + gamma=(self.gamma,), + zfar=(45.0,), + znear=(1.0,), + radius_world=True, + bg_col=torch.ones((3,), dtype=torch.float32, device=DEVICE), + )[0] + + +def cli(): + """ + Scene optimization example using pulsar and the unified PyTorch3D interface. + """ + LOGGER.info("Loading reference...") + # Load reference. + ref = ( + torch.from_numpy( + imageio.imread( + "../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png" + )[:, ::-1, :].copy() + ).to(torch.float32) + / 255.0 + ).to(DEVICE) + # Set up model. + model = SceneModel().to(DEVICE) + # Optimizer. + optimizer = optim.SGD( + [ + {"params": [model.vert_col], "lr": 1e0}, + {"params": [model.vert_rad], "lr": 5e-3}, + {"params": [model.vert_pos], "lr": 1e-2}, + ] + ) + LOGGER.info("Optimizing...") + # Optimize. + for i in range(500): + optimizer.zero_grad() + result = model() + # Visualize. + result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8) + cv2.imshow("opt", result_im[:, :, ::-1]) + overlay_img = np.ascontiguousarray( + ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[ + :, :, ::-1 + ] + ) + overlay_img = cv2.putText( + overlay_img, + "Step %d" % (i), + (10, 40), + cv2.FONT_HERSHEY_SIMPLEX, + 1, + (0, 0, 0), + 2, + cv2.LINE_AA, + False, + ) + cv2.imshow("overlay", overlay_img) + cv2.waitKey(1) + # Update. + loss = ((result - ref) ** 2).sum() + LOGGER.info("loss %d: %f", i, loss.item()) + loss.backward() + optimizer.step() + # Cleanup. + with torch.no_grad(): + model.vert_col.data = torch.clamp(model.vert_col.data, 0.0, 1.0) + # Remove points. + model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0 + model.vert_rad.data[model.vert_rad < 0.001] = 0.0001 + vd = ( + (model.vert_col - torch.ones(3, dtype=torch.float32).to(DEVICE)) + .abs() + .sum(dim=1) + ) + model.vert_pos.data[vd <= 0.2] = -1000.0 + LOGGER.info("Done.") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + cli() diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/data_basics.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/data_basics.rst new file mode 100644 index 0000000000000000000000000000000000000000..3d0bdd8723d27919cfaea511f3dacd79acab34a4 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/data_basics.rst @@ -0,0 +1,29 @@ +pytorch3d.implicitron.dataset in general +======================================== + +Basics of data for implicitron + +.. automodule:: pytorch3d.implicitron.dataset.dataset_base + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.dataset_map_provider + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.data_loader_map_provider + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.data_source + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.scene_batch_sampler + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/datasets.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/datasets.rst new file mode 100644 index 0000000000000000000000000000000000000000..f012fe8b351045fd15ad52673d9e4d258a6ef6d9 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/datasets.rst @@ -0,0 +1,29 @@ +pytorch3d.implicitron.dataset specific datasets +=============================================== + +specific datasets + +.. automodule:: pytorch3d.implicitron.dataset.blender_dataset_map_provider + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.json_index_dataset_map_provider + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.json_index_dataset_map_provider_v2 + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.llff_dataset_map_provider + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.dataset.rendered_mesh_dataset_map_provider + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/evaluation.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/evaluation.rst new file mode 100644 index 0000000000000000000000000000000000000000..094d22e37b52a1bdeb2ad67a05279c5448c58e73 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/evaluation.rst @@ -0,0 +1,14 @@ +pytorch3d.implicitron.evaluation +================================ + +evaluation + +.. automodule:: pytorch3d.implicitron.evaluation.evaluate_new_view_synthesis + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.evaluation.evaluator + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..1fc4fa86f79f5fb4f1d2f941d4b40fa068859a69 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/index.rst @@ -0,0 +1,10 @@ +pytorch3d.implicitron +===================== + +.. toctree:: + + models/index.rst + data_basics + datasets + evaluation + tools diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/base_model.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/base_model.rst new file mode 100644 index 0000000000000000000000000000000000000000..9208d491d631a07fea6c8c5bee8d65b2e3707f19 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/base_model.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.base_model +======================================= + +base_model + +.. automodule:: pytorch3d.implicitron.models.base_model + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/feature_extractor.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/feature_extractor.rst new file mode 100644 index 0000000000000000000000000000000000000000..1121a2014ad25d2b4beffee748307472940ac098 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/feature_extractor.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.feature_extractor.feature_extractor +================================================================ + +feature_extractor + +.. automodule:: pytorch3d.implicitron.models.feature_extractor.feature_extractor + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..51a22ac07e6b8cb309ac8090f91999ac06708bb6 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/index.rst @@ -0,0 +1,7 @@ +pytorch3d.implicitron.models.feature_extractor +============================================== + +.. toctree:: + + feature_extractor + resnet_feature_extractor diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/resnet_feature_extractor.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/resnet_feature_extractor.rst new file mode 100644 index 0000000000000000000000000000000000000000..13d37ed4ea8c827a9c46fdc5f51f1435ad75b7fe --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/feature_extractor/resnet_feature_extractor.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.feature_extractor.resnet_feature_extractor +======================================================================= + +resnet_feature_extractor + +.. automodule:: pytorch3d.implicitron.models.feature_extractor.resnet_feature_extractor + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/generic_model.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/generic_model.rst new file mode 100644 index 0000000000000000000000000000000000000000..41ccc89ac4d8c3c678989225029674a9f1f47253 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/generic_model.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.generic_model +========================================== + +generic_model + +.. automodule:: pytorch3d.implicitron.models.generic_model + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/autodecoder.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/autodecoder.rst new file mode 100644 index 0000000000000000000000000000000000000000..ee7e36c95d0ddd3148bdf4aeae570fbd60ccc8da --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/autodecoder.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.global_encoder.autodecoder +======================================================= + +autodecoder + +.. automodule:: pytorch3d.implicitron.models.global_encoder.autodecoder + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/global_encoder.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/global_encoder.rst new file mode 100644 index 0000000000000000000000000000000000000000..d8c3fb2f06dbefef5022b1c66e919ae7140a36ad --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/global_encoder.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.global_encoder.global_encoder +========================================================== + +global_encoder + +.. automodule:: pytorch3d.implicitron.models.global_encoder.global_encoder + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..b861de01b36ade75b10b78cb721072ec3ca6cd81 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/global_encoder/index.rst @@ -0,0 +1,7 @@ +pytorch3d.implicitron.models.global_encoder +=========================================== + +.. toctree:: + + autodecoder + global_encoder diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/base.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/base.rst new file mode 100644 index 0000000000000000000000000000000000000000..1161b18157d9eb03cef8c4c28a72f6342fe8df8a --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/base.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.base +=================================================== + +base + +.. automodule:: pytorch3d.implicitron.models.implicit_function.base + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/decoding_functions.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/decoding_functions.rst new file mode 100644 index 0000000000000000000000000000000000000000..7a1f135fd894906a0cd05f1fe484c1b20a764e53 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/decoding_functions.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.decoding_functions +================================================================= + +decoding_functions + +.. automodule:: pytorch3d.implicitron.models.implicit_function.decoding_functions + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/idr_feature_field.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/idr_feature_field.rst new file mode 100644 index 0000000000000000000000000000000000000000..8a6d9b5b3c1bfb923b86a3999625b42e955b5dd2 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/idr_feature_field.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.idr_feature_field +================================================================ + +idr_feature_field + +.. automodule:: pytorch3d.implicitron.models.implicit_function.idr_feature_field + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..60fdcd7add50ede8a831479212ae2d340f37194f --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/index.rst @@ -0,0 +1,13 @@ +pytorch3d.implicitron.models.implicit_function +============================================== + +.. toctree:: + + base + decoding_functions + idr_feature_field + neural_radiance_field + scene_representation_networks + utils + voxel_grid + voxel_grid_implicit_function diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/neural_radiance_field.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/neural_radiance_field.rst new file mode 100644 index 0000000000000000000000000000000000000000..7d81e4f81b75f53c14a15950da9c7259fb634024 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/neural_radiance_field.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.neural_radiance_field +==================================================================== + +neural_radiance_field + +.. automodule:: pytorch3d.implicitron.models.implicit_function.neural_radiance_field + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/scene_representation_networks.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/scene_representation_networks.rst new file mode 100644 index 0000000000000000000000000000000000000000..8c9eba8018a50ab3b5ca8b0e3357f1c486f88d86 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/scene_representation_networks.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.scene_representation_networks +============================================================================ + +scene_representation_networks + +.. automodule:: pytorch3d.implicitron.models.implicit_function.scene_representation_networks + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/utils.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/utils.rst new file mode 100644 index 0000000000000000000000000000000000000000..5c8dddb9c4321d798d0261056b9b7996a351a685 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/utils.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.utils +==================================================== + +utils + +.. automodule:: pytorch3d.implicitron.models.implicit_function.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/voxel_grid.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/voxel_grid.rst new file mode 100644 index 0000000000000000000000000000000000000000..137dea9cf4c5b3c473a06bc961db2a836ff6ec92 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/voxel_grid.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.voxel_grid +========================================================= + +voxel_grid + +.. automodule:: pytorch3d.implicitron.models.implicit_function.voxel_grid + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/voxel_grid_implicit_function.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/voxel_grid_implicit_function.rst new file mode 100644 index 0000000000000000000000000000000000000000..5687bc401d83e2e3ed72c02673a6c4e62cbedae3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/implicit_function/voxel_grid_implicit_function.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.implicit_function.voxel_grid_implicit_function +=========================================================================== + +voxel_grid_implicit_function + +.. automodule:: pytorch3d.implicitron.models.implicit_function.voxel_grid_implicit_function + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..4bd70a446c519d92017a5c1fcbbd3f15ebe0a04b --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/index.rst @@ -0,0 +1,15 @@ +pytorch3d.implicitron.models +============================ + +.. toctree:: + + base_model + generic_model + metrics + model_dbir + feature_extractor/index + global_encoder/index + implicit_function/index + renderer/index + view_pooler/index + visualization/index diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/metrics.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/metrics.rst new file mode 100644 index 0000000000000000000000000000000000000000..6895e1ea5dd0759e4e01c1cbd1956159f9faaaea --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/metrics.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.metrics +==================================== + +metrics + +.. automodule:: pytorch3d.implicitron.models.metrics + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/model_dbir.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/model_dbir.rst new file mode 100644 index 0000000000000000000000000000000000000000..c47c784ee9510cf0e6e725eb55d3882be8f77706 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/model_dbir.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.model_dbir +======================================= + +model_dbir + +.. automodule:: pytorch3d.implicitron.models.model_dbir + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/base.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/base.rst new file mode 100644 index 0000000000000000000000000000000000000000..67ad3661ee5253d2d19c22cf46c149af0de10764 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/base.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.base +========================================== + +base + +.. automodule:: pytorch3d.implicitron.models.renderer.base + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..24e7dde7a356123711bf53ce92da87c905d4254e --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/index.rst @@ -0,0 +1,14 @@ +pytorch3d.implicitron.models.renderer +===================================== + +.. toctree:: + + base + lstm_renderer + multipass_ea + ray_point_refiner + ray_sampler + ray_tracing + raymarcher + rgb_net + sdf_renderer diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/lstm_renderer.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/lstm_renderer.rst new file mode 100644 index 0000000000000000000000000000000000000000..f23d985c48e968c275184e289b7483fe4b84ed4d --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/lstm_renderer.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.lstm_renderer +=================================================== + +lstm_renderer + +.. automodule:: pytorch3d.implicitron.models.renderer.lstm_renderer + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/multipass_ea.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/multipass_ea.rst new file mode 100644 index 0000000000000000000000000000000000000000..745c3249e389e96a45133b1aab5e41cb30654dc5 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/multipass_ea.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.multipass_ea +================================================== + +multipass_ea + +.. automodule:: pytorch3d.implicitron.models.renderer.multipass_ea + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_point_refiner.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_point_refiner.rst new file mode 100644 index 0000000000000000000000000000000000000000..f5387ce8ff0d18970c483e56b4a8d340017b9cfb --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_point_refiner.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.ray_point_refiner +======================================================= + +ray_point_refiner + +.. automodule:: pytorch3d.implicitron.models.renderer.ray_point_refiner + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_sampler.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_sampler.rst new file mode 100644 index 0000000000000000000000000000000000000000..5c638abe0e82ad5d6b5b76b6f5353c057bd2bd00 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_sampler.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.ray_sampler +================================================= + +ray_sampler + +.. automodule:: pytorch3d.implicitron.models.renderer.ray_sampler + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_tracing.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_tracing.rst new file mode 100644 index 0000000000000000000000000000000000000000..fe2033df3199bf6af429d3c6f60f3126f0d31b55 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/ray_tracing.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.ray_tracing +================================================= + +ray_tracing + +.. automodule:: pytorch3d.implicitron.models.renderer.ray_tracing + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/raymarcher.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/raymarcher.rst new file mode 100644 index 0000000000000000000000000000000000000000..687202e98b5d4cfe1a5ec4351cf808af4feba636 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/raymarcher.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.raymarcher +================================================ + +raymarcher + +.. automodule:: pytorch3d.implicitron.models.renderer.raymarcher + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/rgb_net.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/rgb_net.rst new file mode 100644 index 0000000000000000000000000000000000000000..dc40dc0799f31d6a22be60d7682d03d13cc37b65 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/rgb_net.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.rgb_net +============================================= + +rgb_net + +.. automodule:: pytorch3d.implicitron.models.renderer.rgb_net + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/sdf_renderer.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/sdf_renderer.rst new file mode 100644 index 0000000000000000000000000000000000000000..ca0e48df8cfe982f2d304aa6fdb3c6f29e5ded02 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/renderer/sdf_renderer.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.renderer.sdf_renderer +================================================== + +sdf_renderer + +.. automodule:: pytorch3d.implicitron.models.renderer.sdf_renderer + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/feature_aggregator.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/feature_aggregator.rst new file mode 100644 index 0000000000000000000000000000000000000000..fb874f03240d5150be6713170a776a9757527639 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/feature_aggregator.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.view_pooler.feature_aggregator +=========================================================== + +feature_aggregator + +.. automodule:: pytorch3d.implicitron.models.view_pooler.feature_aggregator + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..b682259801059263ccbf2d8979950b17a84de6fd --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/index.rst @@ -0,0 +1,8 @@ +pytorch3d.implicitron.models.view_pooler +======================================== + +.. toctree:: + + feature_aggregator + view_pooler + view_sampler diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/view_pooler.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/view_pooler.rst new file mode 100644 index 0000000000000000000000000000000000000000..bfd90434d2a589ea18a047350c6cf55e253e85f7 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/view_pooler.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.view_pooler.view_pooler +==================================================== + +view_pooler + +.. automodule:: pytorch3d.implicitron.models.view_pooler.view_pooler + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/view_sampler.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/view_sampler.rst new file mode 100644 index 0000000000000000000000000000000000000000..eb02e8566fb0f51064b381ddb2c39f6fec1081e3 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/view_pooler/view_sampler.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.view_pooler.view_sampler +===================================================== + +view_sampler + +.. automodule:: pytorch3d.implicitron.models.view_pooler.view_sampler + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/visualization/index.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/visualization/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..ebc041f29773580c929665cb904afef416f53d7c --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/visualization/index.rst @@ -0,0 +1,6 @@ +pytorch3d.implicitron.models.visualization +========================================== + +.. toctree:: + + render_flyaround diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/visualization/render_flyaround.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/visualization/render_flyaround.rst new file mode 100644 index 0000000000000000000000000000000000000000..49779a125357af228e83ebce2bd4d4edd3718fdc --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/models/visualization/render_flyaround.rst @@ -0,0 +1,9 @@ +pytorch3d.implicitron.models.visualization.render_flyaround +=========================================================== + +render_flyaround + +.. automodule:: pytorch3d.implicitron.models.visualization.render_flyaround + :members: + :undoc-members: + :show-inheritance: diff --git a/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/tools.rst b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/tools.rst new file mode 100644 index 0000000000000000000000000000000000000000..a694ed3e4cc282cf7884f2d12870737ffae4d1d9 --- /dev/null +++ b/third_party/AnyBimanual/third_party/pytorch3d/docs/modules/implicitron/tools.rst @@ -0,0 +1,64 @@ +pytorch3d.implicitron.tools +=========================== + +Tools for implicitron + +.. automodule:: pytorch3d.implicitron.tools.camera_utils + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.circle_fitting + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.config + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.eval_video_trajectory + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.image_utils + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.metric_utils + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.model_io + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.point_cloud_utils + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.rasterize_mc + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.stats + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.video_writer + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: pytorch3d.implicitron.tools.vis_utils + :members: + :undoc-members: + :show-inheritance: