| Training & Deploying HOVER Policy | |
| ================================= | |
| This tutorial shows you an example of how to train and deploy HOVER which is a whole-body control (WBC) policy for humanoid robots in the Isaac Lab simulation environment. | |
| It uses the `HOVER`_ repository, which provides an Isaac Lab extension for training neural whole-body control policy for humanoids, as described in the `HOVER Paper`_ and `OMNIH2O Paper`_ papers. | |
| For video demonstrations and more details about the project, please visit the `HOVER Project Website`_ and the `OMNIH2O Project Website`_. | |
| .. figure:: ../../_static/policy_deployment/00_hover/hover_training_robots.png | |
| :align: center | |
| :figwidth: 100% | |
| :alt: visualization of training the policy | |
| Installation | |
| ------------ | |
| .. note:: | |
| This tutorial is for linux only. | |
| HOVER supports Isaac Lab 2.0 and Isaac Sim 4.5. Please ensure you have the correct version of Isaac Lab and Isaac Sim installed to run the HOVER workflow. | |
| 1. Install Isaac Lab following the instructions in the `Isaac Lab Installation Guide`_. | |
| 2. Define the following environment variable to specify the path to your Isaac Lab installation: | |
| .. code-block:: bash | |
| # Set the ISAACLAB_PATH environment variable to point to your Isaac Lab installation directory | |
| export ISAACLAB_PATH=<your_isaac_lab_path> | |
| 3. Clone the `HOVER`_ repository and its submodules in your workspace. | |
| .. code-block:: bash | |
| git clone --recurse-submodules https://github.com/NVlabs/HOVER.git | |
| 4. Install the dependencies. | |
| .. code-block:: bash | |
| cd HOVER | |
| ./install_deps.sh | |
| Training the Policy | |
| ------------------- | |
| Dataset | |
| ~~~~~~~ | |
| Refer to the `HOVER Dataset`_ repository for the steps to obtain and process data for training the policy. | |
| Training the teacher policy | |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |
| Execute the following command from the ``HOVER`` directory to train the teacher policy. | |
| .. code-block:: bash | |
| ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/train_teacher_policy.py \ | |
| --num_envs 1024 \ | |
| --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ | |
| --headless | |
| The teacher policy is trained for 10000000 iterations, or until the user interrupts the training. | |
| The resulting checkpoint is stored in ``neural_wbc/data/data/policy/h1:teacher/`` and the filename is ``model_<iteration_number>.pt``. | |
| Training the student policy | |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |
| Execute the following command from the ``HOVER`` directory to train the student policy using teacher policy checkpoint. | |
| .. code-block:: bash | |
| ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/train_student_policy.py \ | |
| --num_envs 1024 \ | |
| --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ | |
| --teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \ | |
| --teacher_policy.checkpoint model_<iteration_number>.pt \ | |
| --headless | |
| This assumes that you have already trained the teacher policy as there is no provided teacher policy in the repo. | |
| Please refer to these sections on the HOVER repository for more details about training configurations: | |
| - `General Remarks for Training`_ | |
| - `Generalist vs Specialist Policy`_ | |
| Testing the trained policy | |
| -------------------------- | |
| Play teacher policy | |
| ~~~~~~~~~~~~~~~~~~~ | |
| Execute the following command from the ``HOVER`` directory to play the trained teacher policy checkpoint. | |
| .. code-block:: bash | |
| ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/play.py \ | |
| --num_envs 10 \ | |
| --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ | |
| --teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \ | |
| --teacher_policy.checkpoint model_<iteration_number>.pt | |
| Play student policy | |
| ~~~~~~~~~~~~~~~~~~~ | |
| Execute the following command from the ``HOVER`` directory to play the trained student policy checkpoint. | |
| .. code-block:: bash | |
| ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/play.py \ | |
| --num_envs 10 \ | |
| --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ | |
| --student_player \ | |
| --student_path neural_wbc/data/data/policy/h1:student \ | |
| --student_checkpoint model_<iteration_number>.pt | |
| Evaluate the trained policy | |
| --------------------------- | |
| Evaluate the trained policy checkpoint in the Isaac Lab environment. | |
| The evaluation iterates through all the reference motions included in the dataset specified by the ``--reference_motion_path`` option and exits when all motions are evaluated. Randomization is turned off during evaluation. | |
| Refer to the `HOVER Evaluation`_ repository for more details about the evaluation pipeline and the metrics used. | |
| The evaluation script, ``scripts/rsl_rl/eval.py``, uses the same arguments as the play script, ``scripts/rsl_rl/play.py``. You can use it for both teacher and student policies. | |
| .. code-block:: bash | |
| ${ISAACLAB_PATH}/isaaclab.sh -p scripts/rsl_rl/eval.py \ | |
| --num_envs 10 \ | |
| --teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \ | |
| --teacher_policy.checkpoint model_<iteration_number>.pt | |
| Validation of the policy | |
| ------------------------ | |
| The trained policy in Isaac Lab can be validated in another simulation environment or on the real robot. | |
| .. figure:: ../../_static/policy_deployment/00_hover/hover_stable_wave.png | |
| :align: center | |
| :width: 100% | |
| Stable Wave - Mujoco (left) & Real Robot (right) | |
| Sim-to-Sim Validation | |
| ~~~~~~~~~~~~~~~~~~~~~ | |
| Use the provided `Mujoco Environment`_ for conducting sim-to-sim validation of the trained policy. To run the evaluation of Sim2Sim, | |
| .. code-block:: bash | |
| ${ISAACLAB_PATH:?}/isaaclab.sh -p neural_wbc/inference_env/scripts/eval.py \ | |
| --num_envs 1 \ | |
| --headless \ | |
| --student_path neural_wbc/data/data/policy/h1:student/ \ | |
| --student_checkpoint model_<iteration_number>.pt | |
| Please be aware that the mujoco_wrapper only supports one environment at a time. For reference, it will take up to 5h to evaluate 8k reference motions. The inference_env is designed for maximum versatility. | |
| Sim-to-Real Deployment | |
| ~~~~~~~~~~~~~~~~~~~~~~ | |
| For sim-to-real deployment, we provide a `Hardware Environment`_ for `Unitree H1 Robot`_. | |
| Detailed steps of setting up a Sim-to-Real deployment workflow is explained at `README of Sim2Real deployment`_. | |
| To deploy the trained policy on the H1 robot, | |
| .. code-block:: bash | |
| ${ISAACLAB_PATH:?}/isaaclab.sh -p neural_wbc/inference_env/scripts/s2r_player.py \ | |
| --student_path neural_wbc/data/data/policy/h1:student/ \ | |
| --student_checkpoint model_<iteration_number>.pt \ | |
| --reference_motion_path neural_wbc/data/data/motions/<motion_name>.pkl \ | |
| --robot unitree_h1 \ | |
| --max_iterations 5000 \ | |
| --num_envs 1 \ | |
| --headless | |
| .. note:: | |
| The sim-to-real deployment wrapper currently only supports the Unitree H1 robot. It can be extended to other robots by implementing the corresponding hardware wrapper interface. | |
| .. _Isaac Lab Installation Guide: https://isaac-sim.github.io/IsaacLab/v2.0.0/source/setup/installation/index.html | |
| .. _HOVER: https://github.com/NVlabs/HOVER | |
| .. _HOVER Dataset: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#data-processing | |
| .. _HOVER Evaluation: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#evaluation | |
| .. _General Remarks for Training: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#general-remarks-for-training | |
| .. _Generalist vs Specialist Policy: https://github.com/NVlabs/HOVER/?tab=readme-ov-file#generalist-vs-specialist-policy | |
| .. _HOVER Paper: https://arxiv.org/abs/2410.21229 | |
| .. _HOVER Project Website: https://omni.human2humanoid.com/ | |
| .. _OMNIH2O Paper: https://arxiv.org/abs/2410.21229 | |
| .. _OMNIH2O Project Website: https://hover-versatile-humanoid.github.io/ | |
| .. _README of Sim2Real deployment: https://github.com/NVlabs/HOVER/blob/main/neural_wbc/hw_wrappers/README.md | |
| .. _Hardware Environment: https://github.com/NVlabs/HOVER/blob/main/neural_wbc/hw_wrappers/README.md | |
| .. _Mujoco Environment: https://github.com/NVlabs/HOVER/tree/main/neural_wbc/mujoco_wrapper | |
| .. _Unitree H1 Robot: https://unitree.com/h1 | |