Upload folder using huggingface_hub

.gitattributes

@@ -142,3 +142,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 4000/train_state/ocdbt.process_0/d/fb76fcb92bd6e30088fcfe25956ab8c7 filter=lfs diff=lfs merge=lfs -text
 4000/train_state/ocdbt.process_0/d/9a60defbddc71aadce3ed5d3eb7cc4ad filter=lfs diff=lfs merge=lfs -text
 4000/train_state/ocdbt.process_0/d/b4a126dd14bfad0863317f2f3966e36d filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/background_inpaint.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/box_mask_overlay.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/box_world_outline.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/pi_policy_preview_3500.gif filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/pi_policy_preview_3500_strip.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/pi_rollout_preview_3500.gif filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/pi_rollout_preview_3500_strip.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/real_start.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/real_vs_scene_start.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/recorded_motion.gif filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/scene_motion_fit.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/scene_start_render.png filter=lfs diff=lfs merge=lfs -text
+bundle/artifacts/sim/teddy_mask_overlay.png filter=lfs diff=lfs merge=lfs -text

bundle/artifacts/env/hf_env.txt

@@ -0,0 +1,35 @@
+
+Copy-and-paste the text below in your GitHub issue.
+
+- huggingface_hub version: 1.8.0
+- Platform: Linux-6.8.0-59-generic-x86_64-with-glibc2.35
+- Python version: 3.11.10
+- Running in iPython ?: No
+- Running in notebook ?: No
+- Running in Google Colab ?: No
+- Running in Google Colab Enterprise ?: No
+- Token path ?: /workspace/.cache/huggingface/token
+- Has saved token ?: True
+- Who am I ?: lsnu
+- Configured git credential helpers: 
+- Installation method: unknown
+- httpx: 0.27.2
+- hf_xet: 1.4.2
+- gradio: N/A
+- tensorboard: N/A
+- ENDPOINT: https://huggingface.co
+- HF_HUB_CACHE: /workspace/.cache/huggingface/hub
+- HF_ASSETS_CACHE: /workspace/.cache/huggingface/assets
+- HF_TOKEN_PATH: /workspace/.cache/huggingface/token
+- HF_STORED_TOKENS_PATH: /workspace/.cache/huggingface/stored_tokens
+- HF_HUB_OFFLINE: False
+- HF_HUB_DISABLE_TELEMETRY: False
+- HF_HUB_DISABLE_PROGRESS_BARS: None
+- HF_HUB_DISABLE_SYMLINKS_WARNING: False
+- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
+- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
+- HF_HUB_DISABLE_XET: False
+- HF_HUB_ETAG_TIMEOUT: 10
+- HF_HUB_DOWNLOAD_TIMEOUT: 10
+- HF_XET_HIGH_PERFORMANCE: False
+

bundle/artifacts/env/nvidia_smi.txt

@@ -0,0 +1,20 @@
+Sun Mar 29 02:13:09 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 570.133.20             Driver Version: 570.133.20     CUDA Version: 12.8     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA A100-SXM4-80GB          On  |   00000000:90:00.0 Off |                    0 |
+| N/A   43C    P0             66W /  400W |       0MiB /  81920MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|  No running processes found                                                             |
++-----------------------------------------------------------------------------------------+

bundle/artifacts/env/pip_freeze.txt

@@ -0,0 +1,241 @@
+absl-py==2.3.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.4
+aiosignal==1.3.2
+annotated-types==0.7.0
+antlr4-python3-runtime==4.9.3
+asttokens==3.0.0
+attrs==25.3.0
+augmax==0.4.1
+av==14.4.0
+beartype==0.19.0
+beautifulsoup4==4.13.4
+blinker==1.9.0
+cachetools==5.5.2
+certifi==2025.4.26
+cffi==1.17.1
+cfgv==3.4.0
+charset-normalizer==3.4.2
+chex==0.1.89
+click==8.2.1
+cloudpickle==3.1.1
+cmake==4.0.2
+comm==0.2.2
+contourpy==1.3.2
+crc32c==2.7.1
+cycler==0.12.1
+datasets==3.6.0
+debugpy==1.8.14
+decorator==5.2.1
+deepdiff==8.5.0
+diffusers==0.33.1
+dill==0.3.8
+distlib==0.3.9
+dm-control==1.0.14
+dm-env==1.6
+dm-tree==0.1.9
+docker-pycreds==0.4.0
+docstring_parser==0.16
+donfig==0.8.1.post1
+draccus==0.10.0
+einops==0.8.1
+equinox==0.12.2
+etils==1.12.2
+evdev==1.9.2
+executing==2.2.0
+Farama-Notifications==0.0.4
+filelock==3.18.0
+Flask==3.1.1
+flatbuffers==25.2.10
+flax==0.10.2
+fonttools==4.58.1
+frozenlist==1.6.0
+fsspec==2025.3.0
+gcsfs==2025.3.0
+gdown==5.2.0
+gitdb==4.0.12
+GitPython==3.1.44
+glfw==2.9.0
+google-api-core==2.24.2
+google-auth==2.40.2
+google-auth-oauthlib==1.2.2
+google-cloud-core==2.4.3
+google-cloud-storage==3.1.0
+google-crc32c==1.7.1
+google-resumable-media==2.7.2
+googleapis-common-protos==1.70.0
+gym-aloha==0.1.1
+gymnasium==0.29.1
+h5py==3.13.0
+hf-xet==1.1.2
+hf_transfer==0.1.9
+huggingface-hub==0.32.3
+humanize==4.12.3
+identify==2.6.12
+idna==3.10
+imageio==2.37.0
+imageio-ffmpeg==0.6.0
+importlib_metadata==8.7.0
+importlib_resources==6.5.2
+iniconfig==2.1.0
+inquirerpy==0.3.4
+ipykernel==6.29.5
+ipython==9.2.0
+ipython_pygments_lexers==1.1.1
+ipywidgets==8.1.7
+itsdangerous==2.2.0
+jax==0.5.3
+jax-cuda12-pjrt==0.5.3
+jax-cuda12-plugin==0.5.3
+jaxlib==0.5.3
+jaxtyping==0.2.36
+jedi==0.19.2
+Jinja2==3.1.6
+jsonlines==4.0.0
+jupyter_client==8.6.3
+jupyter_core==5.8.1
+jupyterlab_widgets==3.0.15
+kiwisolver==1.4.8
+labmaze==1.0.6
+lerobot @ git+https://github.com/huggingface/lerobot@0cf864870cf29f4738d3ade893e6fd13fbd7cdb5
+llvmlite==0.44.0
+lxml==5.4.0
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.3
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mergedeep==1.3.4
+ml-dtypes==0.4.1
+ml_collections==1.0.0
+mpmath==1.3.0
+msgpack==1.1.0
+mujoco==2.3.7
+multidict==6.4.4
+multiprocess==0.70.16
+mypy_extensions==1.1.0
+nest-asyncio==1.6.0
+networkx==3.5
+nodeenv==1.9.1
+numba==0.61.2
+numcodecs==0.16.1
+numpy==1.26.4
+numpydantic==1.6.9
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cuda-cupti-cu12==12.6.80
+nvidia-cuda-nvcc-cu12==12.9.41
+nvidia-cuda-nvrtc-cu12==12.6.77
+nvidia-cuda-runtime-cu12==12.6.77
+nvidia-cudnn-cu12==9.5.1.17
+nvidia-cufft-cu12==11.3.0.4
+nvidia-cufile-cu12==1.11.1.6
+nvidia-curand-cu12==10.3.7.77
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cusparse-cu12==12.5.4.2
+nvidia-cusparselt-cu12==0.6.3
+nvidia-ml-py==12.575.51
+nvidia-nccl-cu12==2.26.2
+nvidia-nvjitlink-cu12==12.6.85
+nvidia-nvtx-cu12==12.6.77
+oauthlib==3.2.2
+omegaconf==2.3.0
+opencv-python==4.11.0.86
+opencv-python-headless==4.11.0.86
+-e git+https://github.com/Physical-Intelligence/openpi.git@54cbaee6ae0c010a1ed431871cdaa8f4684ac709#egg=openpi
+-e git+https://github.com/Physical-Intelligence/openpi.git@54cbaee6ae0c010a1ed431871cdaa8f4684ac709#egg=openpi_client&subdirectory=packages/openpi-client
+opt_einsum==3.4.0
+optax==0.2.4
+orbax-checkpoint==0.11.13
+orderly-set==5.4.1
+packaging==25.0
+pandas==2.2.3
+parso==0.8.4
+pexpect==4.9.0
+pfzy==0.3.4
+pillow==11.2.1
+platformdirs==4.3.8
+pluggy==1.6.0
+polars==1.30.0
+pre_commit==4.2.0
+prompt_toolkit==3.0.51
+propcache==0.3.1
+proto-plus==1.26.1
+protobuf==4.25.8
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pyarrow==20.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pycparser==2.22
+pydantic==2.11.5
+pydantic_core==2.33.2
+Pygments==2.19.1
+pymunk==7.0.0
+pynput==1.8.1
+pynvml==12.0.0
+PyOpenGL==3.1.9
+pyparsing==3.2.3
+PySocks==1.7.1
+pytest==8.3.5
+python-dateutil==2.9.0.post0
+python-xlib==0.33
+pytz==2025.2
+PyYAML==6.0.2
+pyyaml-include==1.4.1
+pyzmq==26.4.0
+regex==2024.11.6
+requests==2.32.3
+requests-oauthlib==2.0.0
+rerun-sdk==0.23.1
+rich==14.0.0
+rsa==4.9.1
+ruff==0.11.12
+safetensors==0.5.3
+scipy==1.15.3
+sentencepiece==0.2.0
+sentry-sdk==2.29.1
+setproctitle==1.3.6
+shtab==1.7.2
+simplejson==3.20.1
+six==1.17.0
+smmap==5.0.2
+soupsieve==2.7
+stack-data==0.6.3
+svgwrite==1.4.3
+sympy==1.14.0
+tensorstore==0.1.74
+termcolor==3.1.0
+tokenizers==0.21.1
+toml==0.10.2
+toolz==1.0.0
+torch==2.7.1
+torchcodec==0.4.0
+torchvision==0.22.1
+tornado==6.5.1
+tqdm==4.67.1
+tqdm-loggable==0.2
+traitlets==5.14.3
+transformers==4.53.2
+Tree==0.2.4
+treescope==0.1.9
+triton==3.3.1
+typeguard==4.4.2
+typing-inspect==0.9.0
+typing-inspection==0.4.1
+typing_extensions==4.13.2
+tyro==0.9.22
+tzdata==2025.2
+urllib3==2.4.0
+virtualenv==20.31.2
+wadler_lindig==0.1.6
+wandb==0.19.11
+wcwidth==0.2.13
+websockets==15.0.1
+Werkzeug==3.1.3
+widgetsnbextension==4.0.14
+wrapt==1.14.1
+xxhash==3.5.0
+yarl==1.20.0
+zarr==3.0.8
+zipp==3.22.0

bundle/artifacts/env/python_version.txt

@@ -0,0 +1 @@
+Python 3.11.10

bundle/artifacts/env/uv_version.txt

@@ -0,0 +1 @@
+uv 0.11.2 (x86_64-unknown-linux-gnu)

bundle/artifacts/logs/hf_archive.log

@@ -0,0 +1,183 @@
+2026-03-28T22:31:38Z | archiver_start repo=lsnu/TeddyBearKinova root=/workspace/openpi/checkpoints/pi05_kinova_teddybear/teddybear_kinova_ft_v4_b24_w12_fork
+2026-03-28T23:21:41Z | upload_start step=2000
+Repo created: https://huggingface.co/lsnu/TeddyBearKinova
+Found 48 candidate files to upload
+Running validation checks on files to upload...
+Validation checks complete.
+Starting upload...
+You are about to upload a large folder to the Hub using `hf upload-large-folder`. This is a new feature so feedback is very welcome!
+
+A few things to keep in mind:
+  - Repository limits still apply: https://huggingface.co/docs/hub/repositories-recommendations
+  - Do not start several processes in parallel.
+  - You can interrupt and resume the process at any time. The script will pick up where it left off except for partially uploaded files that would have to be entirely reuploaded.
+  - Do not upload the same folder to several repositories. If you need to do so, you must delete the `./.cache/huggingface/` folder first.
+
+Some temporary metadata will be stored under `/workspace/hf_stage_2000/.cache/huggingface`.
+  - You must not modify those files manually.
+  - You must not delete the `./.cache/huggingface/` folder while a process is running.
+  - You can delete the `./.cache/huggingface/` folder to reinitialize the upload state when process is not running. Files will have to be hashed and preuploaded again, except for already committed files.
+
+If the process output is too verbose, you can disable the progress bars with `--no-bars`. You can also entirely disable the status report with `--no-report`.
+
+For more details, run `hf upload-large-folder --help` or check the documentation at https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder.
+
+
+
+---------- 2026-03-28 23:21:44 (0:00:00) ----------
+Files:   hashed 0/48 (0.0/44.8G) | pre-uploaded: 0/0 (0.0/44.8G) (+48 unsure) | committed: 0/48 (0.0/44.8G) | ignored: 0
+Workers: hashing: 8 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
+---------------------------------------------------
+
+---------- 2026-03-28 23:22:44 (0:01:00) ----------
+Files:   hashed 48/48 (44.8G/44.8G) | pre-uploaded: 0/24 (0.0/44.8G) | committed: 0/48 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 8 | committing: 0 | waiting: 0
+---------------------------------------------------
+                             
+---------- 2026-03-28 23:23:44 (0:02:00) ----------
+Files:   hashed 48/48 (44.8G/44.8G) | pre-uploaded: 6/24 (11.5G/44.8G) | committed: 0/48 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 2 | committing: 0 | waiting: 6
+---------------------------------------------------
+                             
+---------- 2026-03-28 23:24:44 (0:03:00) ----------
+Files:   hashed 48/48 (44.8G/44.8G) | pre-uploaded: 24/24 (44.8G/44.8G) | committed: 0/48 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 0 | committing: 1 | waiting: 7
+---------------------------------------------------
+                             All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+Is done: exiting main loop
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+
+---------- 2026-03-28 23:25:03 (0:03:19) ----------
+Files:   hashed 48/48 (44.8G/44.8G) | pre-uploaded: 24/24 (44.8G/44.8G) | committed: 48/48 (44.8G/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
+---------------------------------------------------
+Upload is complete!
+2026-03-28T23:25:10Z | upload_done step=2000
+2026-03-28T23:34:12Z | archiver_start repo=lsnu/TeddyBearKinova root=/workspace/openpi/checkpoints/pi05_kinova_teddybear/teddybear_kinova_ft_v4_b24_w12_fork
+2026-03-28T23:34:32Z | archiver_start repo=lsnu/TeddyBearKinova root=/workspace/openpi/checkpoints/pi05_kinova_teddybear/teddybear_kinova_ft_v4_b24_w12_fork
+2026-03-28T23:56:33Z | upload_start step=2500
+Repo created: https://huggingface.co/lsnu/TeddyBearKinova
+Found 42 candidate files to upload
+Running validation checks on files to upload...
+Validation checks complete.
+Starting upload...
+You are about to upload a large folder to the Hub using `hf upload-large-folder`. This is a new feature so feedback is very welcome!
+
+A few things to keep in mind:
+  - Repository limits still apply: https://huggingface.co/docs/hub/repositories-recommendations
+  - Do not start several processes in parallel.
+  - You can interrupt and resume the process at any time. The script will pick up where it left off except for partially uploaded files that would have to be entirely reuploaded.
+  - Do not upload the same folder to several repositories. If you need to do so, you must delete the `./.cache/huggingface/` folder first.
+
+Some temporary metadata will be stored under `/workspace/hf_stage_2500/.cache/huggingface`.
+  - You must not modify those files manually.
+  - You must not delete the `./.cache/huggingface/` folder while a process is running.
+  - You can delete the `./.cache/huggingface/` folder to reinitialize the upload state when process is not running. Files will have to be hashed and preuploaded again, except for already committed files.
+
+If the process output is too verbose, you can disable the progress bars with `--no-bars`. You can also entirely disable the status report with `--no-report`.
+
+For more details, run `hf upload-large-folder --help` or check the documentation at https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder.
+
+
+
+---------- 2026-03-28 23:56:34 (0:00:00) ----------
+Files:   hashed 0/42 (0.0/44.8G) | pre-uploaded: 0/0 (0.0/44.8G) (+42 unsure) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 8 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
+---------------------------------------------------
+
+---------- 2026-03-28 23:57:34 (0:01:00) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 0/21 (0.0/44.8G) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 7 | committing: 0 | waiting: 1
+---------------------------------------------------
+                             
+---------- 2026-03-28 23:58:34 (0:02:00) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 0/21 (0.0/44.8G) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 7 | committing: 0 | waiting: 1
+---------------------------------------------------
+                             
+---------- 2026-03-28 23:59:35 (0:03:00) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 21/21 (44.8G/44.8G) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 0 | committing: 1 | waiting: 7
+---------------------------------------------------
+                             All files have been processed! Exiting worker.
+Is done: exiting main loop
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+
+---------- 2026-03-28 23:59:58 (0:03:24) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 21/21 (44.8G/44.8G) | committed: 42/42 (44.8G/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
+---------------------------------------------------
+Upload is complete!
+2026-03-29T00:00:05Z | upload_done step=2500
+2026-03-29T00:22:06Z | upload_start step=3000
+Repo created: https://huggingface.co/lsnu/TeddyBearKinova
+Found 42 candidate files to upload
+Running validation checks on files to upload...
+Validation checks complete.
+Starting upload...
+You are about to upload a large folder to the Hub using `hf upload-large-folder`. This is a new feature so feedback is very welcome!
+
+A few things to keep in mind:
+  - Repository limits still apply: https://huggingface.co/docs/hub/repositories-recommendations
+  - Do not start several processes in parallel.
+  - You can interrupt and resume the process at any time. The script will pick up where it left off except for partially uploaded files that would have to be entirely reuploaded.
+  - Do not upload the same folder to several repositories. If you need to do so, you must delete the `./.cache/huggingface/` folder first.
+
+Some temporary metadata will be stored under `/workspace/hf_stage_3000/.cache/huggingface`.
+  - You must not modify those files manually.
+  - You must not delete the `./.cache/huggingface/` folder while a process is running.
+  - You can delete the `./.cache/huggingface/` folder to reinitialize the upload state when process is not running. Files will have to be hashed and preuploaded again, except for already committed files.
+
+If the process output is too verbose, you can disable the progress bars with `--no-bars`. You can also entirely disable the status report with `--no-report`.
+
+For more details, run `hf upload-large-folder --help` or check the documentation at https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder.
+
+
+
+---------- 2026-03-29 00:22:07 (0:00:00) ----------
+Files:   hashed 2/42 (544.0/44.8G) | pre-uploaded: 0/0 (0.0/44.8G) (+42 unsure) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 8 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
+---------------------------------------------------
+
+---------- 2026-03-29 00:23:07 (0:01:00) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 0/20 (0.0/44.8G) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 6 | committing: 0 | waiting: 2
+---------------------------------------------------
+                             
+---------- 2026-03-29 00:24:07 (0:02:00) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 6/20 (11.5G/44.8G) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 2 | committing: 0 | waiting: 6
+---------------------------------------------------
+                             
+---------- 2026-03-29 00:25:07 (0:03:00) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 20/20 (44.8G/44.8G) | committed: 0/42 (0.0/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 0 | committing: 1 | waiting: 7
+---------------------------------------------------
+                             All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+Is done: exiting main loop
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+All files have been processed! Exiting worker.
+
+---------- 2026-03-29 00:25:28 (0:03:20) ----------
+Files:   hashed 42/42 (44.8G/44.8G) | pre-uploaded: 20/20 (44.8G/44.8G) | committed: 42/42 (44.8G/44.8G) | ignored: 0
+Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 0 | committing: 0 | waiting: 0
+---------------------------------------------------
+Upload is complete!
+2026-03-29T00:25:35Z | upload_done step=3000

bundle/artifacts/logs/hf_archived_steps.txt

@@ -0,0 +1,4 @@
+1000
+2000
+2500
+3000

bundle/artifacts/logs/stop_at_4000.log

@@ -0,0 +1,4 @@
+2026-03-29T01:29:49Z | watch_start pid=1221991 target_step=4000 checkpoint_root=/workspace/openpi/checkpoints/pi05_kinova_teddybear/teddybear_kinova_ft_v4_b24_w12_fork
+2026-03-29T01:51:22Z | checkpoint_ready step=4000
+2026-03-29T01:51:22Z | signal_sent signal=TERM pid=1221991
+2026-03-29T01:51:28Z | signal_sent signal=INT pid=1221991

bundle/artifacts/sim/pi_actions_3500.json

@@ -0,0 +1,162 @@
+{
+  "checkpoint_dir": "/workspace/kinova_scene_sim/staged_checkpoints_3500/3500",
+  "session_root": "/workspace/data/teddybear_raw/session_20260327_165944_bear",
+  "sync_row_index": 0,
+  "azure_rgb_file": "azure_rgb/000010.jpg",
+  "wrist_rgb_file": "rgb/000000.jpg",
+  "state": [
+    0.13042281568050385,
+    0.0013459887122735381,
+    0.2086409330368042,
+    176.00607299804688,
+    -0.0013248149771243334,
+    89.99845886230469,
+    4.80349588394165
+  ],
+  "actions": [
+    [
+      0.13038688898086548,
+      0.0014187133638188243,
+      0.20857597887516022,
+      176.00660705566406,
+      -0.0010383824119344354,
+      89.99771881103516,
+      3.870360851287842
+    ],
+    [
+      0.1302502304315567,
+      0.0013153755571693182,
+      0.20861351490020752,
+      176.0064697265625,
+      -0.0011160902213305235,
+      89.99806213378906,
+      3.690521240234375
+    ],
+    [
+      0.13023793697357178,
+      0.0012819102266803384,
+      0.20857881009578705,
+      176.0062713623047,
+      -0.0012181210331618786,
+      89.99817657470703,
+      3.651444911956787
+    ],
+    [
+      0.13030986487865448,
+      0.0013734204694628716,
+      0.20860803127288818,
+      176.0064239501953,
+      -0.0014578866539523005,
+      89.99809265136719,
+      3.6906394958496094
+    ],
+    [
+      0.1302148997783661,
+      0.0013296795077621937,
+      0.20862483978271484,
+      176.0063934326172,
+      -0.0012297590728849173,
+      89.9979476928711,
+      3.66821551322937
+    ],
+    [
+      0.13030855357646942,
+      0.0012651914730668068,
+      0.20867392420768738,
+      176.00656127929688,
+      -0.001598228933289647,
+      89.99803161621094,
+      3.754732847213745
+    ],
+    [
+      0.13031162321567535,
+      0.0012794281356036663,
+      0.20865340530872345,
+      176.00653076171875,
+      -0.0015227764379233122,
+      89.99803924560547,
+      3.8800547122955322
+    ],
+    [
+      0.13022325932979584,
+      0.0012262801174074411,
+      0.20850715041160583,
+      176.006103515625,
+      -0.0012301492970436811,
+      89.99808502197266,
+      4.038285732269287
+    ],
+    [
+      0.13038043677806854,
+      0.001231524394825101,
+      0.20850330591201782,
+      176.00624084472656,
+      -0.0012325852876529098,
+      89.9980697631836,
+      4.302825927734375
+    ],
+    [
+      0.13038581609725952,
+      0.001150989904999733,
+      0.20857061445713043,
+      176.0060577392578,
+      -0.0012798609677702188,
+      89.9980239868164,
+      4.259771347045898
+    ],
+    [
+      0.13041627407073975,
+      0.0011642909375950694,
+      0.20851978659629822,
+      176.006103515625,
+      -0.0012036851840093732,
+      89.99813842773438,
+      4.265687465667725
+    ],
+    [
+      0.13035060465335846,
+      0.0012715674238279462,
+      0.20847438275814056,
+      176.00587463378906,
+      -0.0015258120838552713,
+      89.99850463867188,
+      4.377945423126221
+    ],
+    [
+      0.1303417980670929,
+      0.0012600033078342676,
+      0.2084597498178482,
+      176.0060272216797,
+      -0.0009475614642724395,
+      89.99833679199219,
+      4.183290481567383
+    ],
+    [
+      0.1304168999195099,
+      0.0011939689284190536,
+      0.20853134989738464,
+      176.0057830810547,
+      -0.0013050022535026073,
+      89.99844360351562,
+      4.19566011428833
+    ],
+    [
+      0.1303562968969345,
+      0.0012072586687281728,
+      0.20855338871479034,
+      176.00575256347656,
+      -0.000863268505781889,
+      89.99850463867188,
+      4.2605509757995605
+    ],
+    [
+      0.1303006112575531,
+      0.0012522602919489145,
+      0.2085985243320465,
+      176.0056915283203,
+      -0.0008069146424531937,
+      89.99847412109375,
+      4.497114658355713
+    ]
+  ]
+}

bundle/artifacts/sim/pi_rollout_3500.json

@@ -0,0 +1,3967 @@
+{
+  "checkpoint_dir": "/workspace/kinova_scene_sim/staged_checkpoints_3500/3500",
+  "session_root": "/workspace/data/teddybear_raw/session_20260327_165944_bear",
+  "sync_row_index": 0,
+  "azure_rgb_file": "azure_rgb/000010.jpg",
+  "wrist_rgb_file": "rgb/000000.jpg",
+  "initial_state": [
+    0.13042281568050385,
+    0.0013459887122735381,
+    0.2086409330368042,
+    176.00607299804688,
+    -0.0013248149771243334,
+    89.99845886230469,
+    4.80349588394165
+  ],
+  "states": [
+    [
+      0.13042281568050385,
+      0.0013459887122735381,
+      0.2086409330368042,
+      176.00607299804688,
+      -0.0013248149771243334,
+      89.99845886230469,
+      4.80349588394165
+    ],
+    [
+      0.13038688898086548,
+      0.0014187133638188243,
+      0.20857597887516022,
+      176.00660705566406,
+      -0.0010383824119344354,
+      89.99771881103516,
+      3.870360851287842
+    ],
+    [
+      0.13043420016765594,
+      0.0016596653731539845,
+      0.20861729979515076,
+      176.0086669921875,
+      -0.0006642318912781775,
+      89.99720001220703,
+      3.328033208847046
+    ],
+    [
+      0.13064095377922058,
+      0.0016947118565440178,
+      0.20848169922828674,
+      176.00949096679688,
+      -0.00016581840463913977,
+      89.99673461914062,
+      4.218804359436035
+    ],
+    [
+      0.13079629838466644,
+      0.0017693787813186646,
+      0.20835432410240173,
+      176.0095672607422,
+      -0.0001273146626772359,
+      89.9961166381836,
+      3.5781540870666504
+    ],
+    [
+      0.1309470683336258,
+      0.0018013687804341316,
+      0.20847514271736145,
+      176.0099639892578,
+      -5.566468462347984e-05,
+      89.99688720703125,
+      2.834836006164551
+    ],
+    [
+      0.13090649247169495,
+      0.001984028145670891,
+      0.2085830420255661,
+      176.0093536376953,
+      9.991625847760588e-05,
+      89.99685668945312,
+      1.941438913345337
+    ],
+    [
+      0.1310153752565384,
+      0.0020484186243265867,
+      0.20867235958576202,
+      176.0103759765625,
+      7.498337072320282e-05,
+      89.9969482421875,
+      2.07706880569458
+    ],
+    [
+      0.13114196062088013,
+      0.0021104831248521805,
+      0.20861603319644928,
+      176.01026916503906,
+      0.0004441899945959449,
+      89.99708557128906,
+      1.6497399806976318
+    ],
+    [
+      0.1310625970363617,
+      0.002262422349303961,
+      0.2089119255542755,
+      176.0103759765625,
+      0.0006257580826058984,
+      89.99360656738281,
+      1.7586634159088135
+    ],
+    [
+      0.13105656206607819,
+      0.0023602405562996864,
+      0.20898427069187164,
+      176.01063537597656,
+      0.0006020718137733638,
+      89.9943618774414,
+      2.179922342300415
+    ],
+    [
+      0.1311476230621338,
+      0.0023023055400699377,
+      0.20911815762519836,
+      176.01226806640625,
+      0.00027950218645855784,
+      89.9934310913086,
+      1.5571088790893555
+    ],
+    [
+      0.13151052594184875,
+      0.002042755950242281,
+      0.20936307311058044,
+      176.01976013183594,
+      0.00022363767493516207,
+      89.9924545288086,
+      1.3097466230392456
+    ],
+    [
+      0.1314568668603897,
+      0.0019841394387185574,
+      0.20942673087120056,
+      176.0360870361328,
+      0.0015758902300149202,
+      89.99384307861328,
+      1.5428340435028076
+    ],
+    [
+      0.13194192945957184,
+      0.0020381570793688297,
+      0.20943905413150787,
+      176.05006408691406,
+      0.0034663670230656862,
+      89.99756622314453,
+      1.0901957750320435
+    ],
+    [
+      0.13395386934280396,
+      0.0022533698938786983,
+      0.2094932496547699,
+      176.0591278076172,
+      0.00533080380409956,
+      90.00312805175781,
+      1.1050819158554077
+    ],
+    [
+      0.13620370626449585,
+      0.0022139614447951317,
+      0.20934607088565826,
+      176.06236267089844,
+      0.005754353944212198,
+      90.00737762451172,
+      0.46630996465682983
+    ],
+    [
+      0.1415129005908966,
+      0.0027319975197315216,
+      0.2093348205089569,
+      176.06459045410156,
+      0.0035483501851558685,
+      90.00851440429688,
+      0.9952843189239502
+    ],
+    [
+      0.1459393948316574,
+      0.0026647618506103754,
+      0.2093164324760437,
+      176.06600952148438,
+      0.00192162801977247,
+      90.01443481445312,
+      0.44950979948043823
+    ],
+    [
+      0.14900004863739014,
+      0.002921953797340393,
+      0.20949354767799377,
+      176.06668090820312,
+      0.001516735297627747,
+      90.01408386230469,
+      0.3026089072227478
+    ],
+    [
+      0.15320706367492676,
+      0.003877518232911825,
+      0.2095641791820526,
+      176.0668487548828,
+      -0.001166658359579742,
+      90.01170349121094,
+      0.6711252927780151
+    ],
+    [
+      0.15570738911628723,
+      0.004031960386782885,
+      0.20950137078762054,
+      176.06748962402344,
+      0.0008320023771375418,
+      90.02385711669922,
+      0.26037225127220154
+    ],
+    [
+      0.15965940058231354,
+      0.0043397825211286545,
+      0.209621861577034,
+      176.0689239501953,
+      -0.0011879531666636467,
+      90.02320098876953,
+      0.6312397122383118
+    ],
+    [
+      0.1627015769481659,
+      0.004767170641571283,
+      0.20974454283714294,
+      176.0701446533203,
+      -0.002045096829533577,
+      90.0233154296875,
+      0.7582746148109436
+    ],
+    [
+      0.1651243418455124,
+      0.004650649148970842,
+      0.2099265307188034,
+      176.0723419189453,
+      -0.0020860014483332634,
+      90.02278900146484,
+      0.6095306873321533
+    ]
+  ],
+  "actions": [
+    [
+      0.13038688898086548,
+      0.0014187133638188243,
+      0.20857597887516022,
+      176.00660705566406,
+      -0.0010383824119344354,
+      89.99771881103516,
+      3.870360851287842
+    ],
+    [
+      0.13043420016765594,
+      0.0016596653731539845,
+      0.20861729979515076,
+      176.0086669921875,
+      -0.0006642318912781775,
+      89.99720001220703,
+      3.328033208847046
+    ],
+    [
+      0.13064095377922058,
+      0.0016947118565440178,
+      0.20848169922828674,
+      176.00949096679688,
+      -0.00016581840463913977,
+      89.99673461914062,
+      4.218804359436035
+    ],
+    [
+      0.13079629838466644,
+      0.0017693787813186646,
+      0.20835432410240173,
+      176.0095672607422,
+      -0.0001273146626772359,
+      89.9961166381836,
+      3.5781540870666504
+    ],
+    [
+      0.1309470683336258,
+      0.0018013687804341316,
+      0.20847514271736145,
+      176.0099639892578,
+      -5.566468462347984e-05,
+      89.99688720703125,
+      2.834836006164551
+    ],
+    [
+      0.13090649247169495,
+      0.001984028145670891,
+      0.2085830420255661,
+      176.0093536376953,
+      9.991625847760588e-05,
+      89.99685668945312,
+      1.941438913345337
+    ],
+    [
+      0.1310153752565384,
+      0.0020484186243265867,
+      0.20867235958576202,
+      176.0103759765625,
+      7.498337072320282e-05,
+      89.9969482421875,
+      2.07706880569458
+    ],
+    [
+      0.13114196062088013,
+      0.0021104831248521805,
+      0.20861603319644928,
+      176.01026916503906,
+      0.0004441899945959449,
+      89.99708557128906,
+      1.6497399806976318
+    ],
+    [
+      0.1310625970363617,
+      0.002262422349303961,
+      0.2089119255542755,
+      176.0103759765625,
+      0.0006257580826058984,
+      89.99360656738281,
+      1.7586634159088135
+    ],
+    [
+      0.13105656206607819,
+      0.0023602405562996864,
+      0.20898427069187164,
+      176.01063537597656,
+      0.0006020718137733638,
+      89.9943618774414,
+      2.179922342300415
+    ],
+    [
+      0.1311476230621338,
+      0.0023023055400699377,
+      0.20911815762519836,
+      176.01226806640625,
+      0.00027950218645855784,
+      89.9934310913086,
+      1.5571088790893555
+    ],
+    [
+      0.13151052594184875,
+      0.002042755950242281,
+      0.20936307311058044,
+      176.01976013183594,
+      0.00022363767493516207,
+      89.9924545288086,
+      1.3097466230392456
+    ],
+    [
+      0.1314568668603897,
+      0.0019841394387185574,
+      0.20942673087120056,
+      176.0360870361328,
+      0.0015758902300149202,
+      89.99384307861328,
+      1.5428340435028076
+    ],
+    [
+      0.13194192945957184,
+      0.0020381570793688297,
+      0.20943905413150787,
+      176.05006408691406,
+      0.0034663670230656862,
+      89.99756622314453,
+      1.0901957750320435
+    ],
+    [
+      0.13395386934280396,
+      0.0022533698938786983,
+      0.2094932496547699,
+      176.0591278076172,
+      0.00533080380409956,
+      90.00312805175781,
+      1.1050819158554077
+    ],
+    [
+      0.13620370626449585,
+      0.0022139614447951317,
+      0.20934607088565826,
+      176.06236267089844,
+      0.005754353944212198,
+      90.00737762451172,
+      0.46630996465682983
+    ],
+    [
+      0.1415129005908966,
+      0.0027319975197315216,
+      0.2093348205089569,
+      176.06459045410156,
+      0.0035483501851558685,
+      90.00851440429688,
+      0.9952843189239502
+    ],
+    [
+      0.1459393948316574,
+      0.0026647618506103754,
+      0.2093164324760437,
+      176.06600952148438,
+      0.00192162801977247,
+      90.01443481445312,
+      0.44950979948043823
+    ],
+    [
+      0.14900004863739014,
+      0.002921953797340393,
+      0.20949354767799377,
+      176.06668090820312,
+      0.001516735297627747,
+      90.01408386230469,
+      0.3026089072227478
+    ],
+    [
+      0.15320706367492676,
+      0.003877518232911825,
+      0.2095641791820526,
+      176.0668487548828,
+      -0.001166658359579742,
+      90.01170349121094,
+      0.6711252927780151
+    ],
+    [
+      0.15570738911628723,
+      0.004031960386782885,
+      0.20950137078762054,
+      176.06748962402344,
+      0.0008320023771375418,
+      90.02385711669922,
+      0.26037225127220154
+    ],
+    [
+      0.15965940058231354,
+      0.0043397825211286545,
+      0.209621861577034,
+      176.0689239501953,
+      -0.0011879531666636467,
+      90.02320098876953,
+      0.6312397122383118
+    ],
+    [
+      0.1627015769481659,
+      0.004767170641571283,
+      0.20974454283714294,
+      176.0701446533203,
+      -0.002045096829533577,
+      90.0233154296875,
+      0.7582746148109436
+    ],
+    [
+      0.1651243418455124,
+      0.004650649148970842,
+      0.2099265307188034,
+      176.0723419189453,
+      -0.0020860014483332634,
+      90.02278900146484,
+      0.6095306873321533
+    ]
+  ],
+  "chunks": [
+    [
+      [
+        0.13038688898086548,
+        0.0014187133638188243,
+        0.20857597887516022,
+        176.00660705566406,
+        -0.0010383824119344354,
+        89.99771881103516,
+        3.870360851287842
+      ],
+      [
+        0.1302502304315567,
+        0.0013153755571693182,
+        0.20861351490020752,
+        176.0064697265625,
+        -0.0011160902213305235,
+        89.99806213378906,
+        3.690521240234375
+      ],
+      [
+        0.13023793697357178,
+        0.0012819102266803384,
+        0.20857881009578705,
+        176.0062713623047,
+        -0.0012181210331618786,
+        89.99817657470703,
+        3.651444911956787
+      ],
+      [
+        0.13030986487865448,
+        0.0013734204694628716,
+        0.20860803127288818,
+        176.0064239501953,
+        -0.0014578866539523005,
+        89.99809265136719,
+        3.6906394958496094
+      ],
+      [
+        0.1302148997783661,
+        0.0013296795077621937,
+        0.20862483978271484,
+        176.0063934326172,
+        -0.0012297590728849173,
+        89.9979476928711,
+        3.66821551322937
+      ],
+      [
+        0.13030855357646942,
+        0.0012651914730668068,
+        0.20867392420768738,
+        176.00656127929688,
+        -0.001598228933289647,
+        89.99803161621094,
+        3.754732847213745
+      ],
+      [
+        0.13031162321567535,
+        0.0012794281356036663,
+        0.20865340530872345,
+        176.00653076171875,
+        -0.0015227764379233122,
+        89.99803924560547,
+        3.8800547122955322
+      ],
+      [
+        0.13022325932979584,
+        0.0012262801174074411,
+        0.20850715041160583,
+        176.006103515625,
+        -0.0012301492970436811,
+        89.99808502197266,
+        4.038285732269287
+      ],
+      [
+        0.13038043677806854,
+        0.001231524394825101,
+        0.20850330591201782,
+        176.00624084472656,
+        -0.0012325852876529098,
+        89.9980697631836,
+        4.302825927734375
+      ],
+      [
+        0.13038581609725952,
+        0.001150989904999733,
+        0.20857061445713043,
+        176.0060577392578,
+        -0.0012798609677702188,
+        89.9980239868164,
+        4.259771347045898
+      ],
+      [
+        0.13041627407073975,
+        0.0011642909375950694,
+        0.20851978659629822,
+        176.006103515625,
+        -0.0012036851840093732,
+        89.99813842773438,
+        4.265687465667725
+      ],
+      [
+        0.13035060465335846,
+        0.0012715674238279462,
+        0.20847438275814056,
+        176.00587463378906,
+        -0.0015258120838552713,
+        89.99850463867188,
+        4.377945423126221
+      ],
+      [
+        0.1303417980670929,
+        0.0012600033078342676,
+        0.2084597498178482,
+        176.0060272216797,
+        -0.0009475614642724395,
+        89.99833679199219,
+        4.183290481567383
+      ],
+      [
+        0.1304168999195099,
+        0.0011939689284190536,
+        0.20853134989738464,
+        176.0057830810547,
+        -0.0013050022535026073,
+        89.99844360351562,
+        4.19566011428833
+      ],
+      [
+        0.1303562968969345,
+        0.0012072586687281728,
+        0.20855338871479034,
+        176.00575256347656,
+        -0.000863268505781889,
+        89.99850463867188,
+        4.2605509757995605
+      ],
+      [
+        0.1303006112575531,
+        0.0012522602919489145,
+        0.2085985243320465,
+        176.0056915283203,
+        -0.0008069146424531937,
+        89.99847412109375,
+        4.497114658355713
+      ]
+    ],
+    [
+      [
+        0.13043420016765594,
+        0.0016596653731539845,
+        0.20861729979515076,
+        176.0086669921875,
+        -0.0006642318912781775,
+        89.99720001220703,
+        3.328033208847046
+      ],
+      [
+        0.1304139941930771,
+        0.0017380370991304517,
+        0.20870471000671387,
+        176.00819396972656,
+        -0.00017333835421595722,
+        89.99736785888672,
+        3.3828375339508057
+      ],
+      [
+        0.1303291618824005,
+        0.0016114477766677737,
+        0.20845387876033783,
+        176.0089569091797,
+        -0.0009894894901663065,
+        89.99771118164062,
+        3.255220651626587
+      ],
+      [
+        0.13032270967960358,
+        0.0016015756409615278,
+        0.20840081572532654,
+        176.00868225097656,
+        -0.001061268849298358,
+        89.99730682373047,
+        3.231813430786133
+      ],
+      [
+        0.13036927580833435,
+        0.0014416162157431245,
+        0.20839662849903107,
+        176.00863647460938,
+        -0.0007507895934395492,
+        89.99756622314453,
+        3.0640480518341064
+      ],
+      [
+        0.13038352131843567,
+        0.0017369053093716502,
+        0.20851343870162964,
+        176.0083465576172,
+        -0.0008427334250882268,
+        89.9976806640625,
+        2.7776923179626465
+      ],
+      [
+        0.130374938249588,
+        0.001516032381914556,
+        0.2085302770137787,
+        176.00863647460938,
+        -0.0011493967613205314,
+        89.99757385253906,
+        2.808537006378174
+      ],
+      [
+        0.13043144345283508,
+        0.001582918455824256,
+        0.20852799713611603,
+        176.00869750976562,
+        -0.000853752950206399,
+        89.99739074707031,
+        2.620037317276001
+      ],
+      [
+        0.13045990467071533,
+        0.0014022847171872854,
+        0.20849116146564484,
+        176.0091094970703,
+        -0.001233889488503337,
+        89.99779510498047,
+        2.669640302658081
+      ],
+      [
+        0.1303248256444931,
+        0.0014256987487897277,
+        0.2084859311580658,
+        176.00872802734375,
+        -0.0007936973706819117,
+        89.99757385253906,
+        2.7151496410369873
+      ],
+      [
+        0.13034434616565704,
+        0.0014580099377781153,
+        0.20850197970867157,
+        176.0086212158203,
+        -0.0008582730661146343,
+        89.99765014648438,
+        2.4230668544769287
+      ],
+      [
+        0.13033334910869598,
+        0.001468123053200543,
+        0.20849834382534027,
+        176.00881958007812,
+        -0.0007506563561037183,
+        89.99785614013672,
+        2.4433109760284424
+      ],
+      [
+        0.1304265856742859,
+        0.0015017733676359057,
+        0.2084486335515976,
+        176.00889587402344,
+        -0.0007904047961346805,
+        89.99774932861328,
+        2.1915478706359863
+      ],
+      [
+        0.13039429485797882,
+        0.001430780510418117,
+        0.20852449536323547,
+        176.00917053222656,
+        -0.000892863841727376,
+        89.99761199951172,
+        2.408724069595337
+      ],
+      [
+        0.13038116693496704,
+        0.001403976697474718,
+        0.20847949385643005,
+        176.00889587402344,
+        -0.0008508600876666605,
+        89.99790954589844,
+        2.461463689804077
+      ],
+      [
+        0.13030168414115906,
+        0.0013514338061213493,
+        0.2085845023393631,
+        176.0089569091797,
+        -0.0007433861610479653,
+        89.99764251708984,
+        2.468567132949829
+      ]
+    ],
+    [
+      [
+        0.13064095377922058,
+        0.0016947118565440178,
+        0.20848169922828674,
+        176.00949096679688,
+        -0.00016581840463913977,
+        89.99673461914062,
+        4.218804359436035
+      ],
+      [
+        0.1306656301021576,
+        0.001803360995836556,
+        0.20845556259155273,
+        176.0099639892578,
+        -0.0004244258161634207,
+        89.99695587158203,
+        4.156636714935303
+      ],
+      [
+        0.13070720434188843,
+        0.0018559599993750453,
+        0.20850147306919098,
+        176.0098419189453,
+        -0.0005103364819660783,
+        89.99713134765625,
+        4.380053997039795
+      ],
+      [
+        0.13052570819854736,
+        0.0018253352027386427,
+        0.20836898684501648,
+        176.0098419189453,
+        -0.0006624595844186842,
+        89.99700164794922,
+        4.376288414001465
+      ],
+      [
+        0.13039222359657288,
+        0.0015040939906612039,
+        0.20878161489963531,
+        176.01002502441406,
+        -0.0005345261888578534,
+        89.99716186523438,
+        4.6692986488342285
+      ],
+      [
+        0.13047470152378082,
+        0.0017413381719961762,
+        0.20889818668365479,
+        176.010498046875,
+        -0.0004075253673363477,
+        89.99696350097656,
+        4.750035285949707
+      ],
+      [
+        0.13063737750053406,
+        0.0015569338575005531,
+        0.20856650173664093,
+        176.01034545898438,
+        -0.0009047851199284196,
+        89.99739074707031,
+        4.462838172912598
+      ],
+      [
+        0.13045653700828552,
+        0.0014287366066128016,
+        0.20865672826766968,
+        176.00982666015625,
+        -0.0005488002207130194,
+        89.99681091308594,
+        4.558866024017334
+      ],
+      [
+        0.1306876540184021,
+        0.001681859022937715,
+        0.20862530171871185,
+        176.0103302001953,
+        -0.0005408638389781117,
+        89.99718475341797,
+        4.885012626647949
+      ],
+      [
+        0.13046996295452118,
+        0.0018679274944588542,
+        0.20882092416286469,
+        176.00994873046875,
+        -0.0004903528024442494,
+        89.99720764160156,
+        4.507809638977051
+      ],
+      [
+        0.13079078495502472,
+        0.0016777466516941786,
+        0.20873045921325684,
+        176.00924682617188,
+        0.00021807696612086147,
+        89.99684143066406,
+        4.9376373291015625
+      ],
+      [
+        0.130674809217453,
+        0.001853483496233821,
+        0.20840691030025482,
+        176.00921630859375,
+        0.00014381392975337803,
+        89.9966812133789,
+        4.486833095550537
+      ],
+      [
+        0.13063739240169525,
+        0.001660753390751779,
+        0.20858752727508545,
+        176.0089569091797,
+        1.834879299167369e-06,
+        89.99697875976562,
+        4.827036380767822
+      ],
+      [
+        0.13051098585128784,
+        0.0016984489047899842,
+        0.208602637052536,
+        176.0093994140625,
+        -8.295287989312783e-05,
+        89.9972152709961,
+        4.797352313995361
+      ],
+      [
+        0.13057023286819458,
+        0.0015257655177265406,
+        0.2086484283208847,
+        176.0095977783203,
+        -0.00019454726134426892,
+        89.99728393554688,
+        4.569445610046387
+      ],
+      [
+        0.13049821555614471,
+        0.001634918968193233,
+        0.20886337757110596,
+        176.00965881347656,
+        -0.000377635529730469,
+        89.9977035522461,
+        4.487051486968994
+      ]
+    ],
+    [
+      [
+        0.13079629838466644,
+        0.0017693787813186646,
+        0.20835432410240173,
+        176.0095672607422,
+        -0.0001273146626772359,
+        89.9961166381836,
+        3.5781540870666504
+      ],
+      [
+        0.13059517741203308,
+        0.0016042535426095128,
+        0.20844927430152893,
+        176.0094757080078,
+        -9.113474516198039e-05,
+        89.99629974365234,
+        3.5728580951690674
+      ],
+      [
+        0.1305704563856125,
+        0.0016339595895260572,
+        0.20854619145393372,
+        176.0096893310547,
+        -0.00019901788618881255,
+        89.99674224853516,
+        3.449205160140991
+      ],
+      [
+        0.1305752545595169,
+        0.001851420383900404,
+        0.20864161849021912,
+        176.00965881347656,
+        -9.652081644162536e-05,
+        89.996826171875,
+        3.4286983013153076
+      ],
+      [
+        0.13053040206432343,
+        0.0018075169064104557,
+        0.2086752951145172,
+        176.00955200195312,
+        -0.00028139809728600085,
+        89.99659729003906,
+        3.219998836517334
+      ],
+      [
+        0.13060849905014038,
+        0.001731313532218337,
+        0.20848137140274048,
+        176.00904846191406,
+        -0.000442685151938349,
+        89.99694061279297,
+        3.2834484577178955
+      ],
+      [
+        0.13063274323940277,
+        0.0016856620786711574,
+        0.20840498805046082,
+        176.00949096679688,
+        -0.0005296520539559424,
+        89.99668884277344,
+        3.234495162963867
+      ],
+      [
+        0.1308305859565735,
+        0.0017480265814810991,
+        0.2083055078983307,
+        176.0096435546875,
+        -0.00025680867838673294,
+        89.99659729003906,
+        3.2613022327423096
+      ],
+      [
+        0.13056963682174683,
+        0.0017820186913013458,
+        0.20845040678977966,
+        176.00946044921875,
+        -0.00040342204738408327,
+        89.9967269897461,
+        3.3895716667175293
+      ],
+      [
+        0.13065794110298157,
+        0.0015162450727075338,
+        0.20849759876728058,
+        176.00912475585938,
+        -0.00039509552880190313,
+        89.99674224853516,
+        3.229161024093628
+      ],
+      [
+        0.13065195083618164,
+        0.0016540735960006714,
+        0.20853367447853088,
+        176.009033203125,
+        -0.0003080239112023264,
+        89.99667358398438,
+        3.134122610092163
+      ],
+      [
+        0.13061147928237915,
+        0.001768062124028802,
+        0.2086455225944519,
+        176.0088348388672,
+        -4.922618609271012e-05,
+        89.99713134765625,
+        3.056776285171509
+      ],
+      [
+        0.13046883046627045,
+        0.001975113060325384,
+        0.20840057730674744,
+        176.00938415527344,
+        -0.00021784058480989188,
+        89.99690246582031,
+        3.0827386379241943
+      ],
+      [
+        0.13062995672225952,
+        0.001725139212794602,
+        0.20852205157279968,
+        176.0091094970703,
+        -0.00044183823047205806,
+        89.99711608886719,
+        2.915327787399292
+      ],
+      [
+        0.13046248257160187,
+        0.0016581580275669694,
+        0.20843657851219177,
+        176.0091094970703,
+        -0.00010100286453962326,
+        89.99694061279297,
+        3.080239772796631
+      ],
+      [
+        0.1306220293045044,
+        0.0016375004779547453,
+        0.20845238864421844,
+        176.00936889648438,
+        -0.00038476113695651293,
+        89.99683380126953,
+        3.1575357913970947
+      ]
+    ],
+    [
+      [
+        0.1309470683336258,
+        0.0018013687804341316,
+        0.20847514271736145,
+        176.0099639892578,
+        -5.566468462347984e-05,
+        89.99688720703125,
+        2.834836006164551
+      ],
+      [
+        0.13086992502212524,
+        0.0018532057292759418,
+        0.20829977095127106,
+        176.0097198486328,
+        6.503488293674309e-06,
+        89.99710845947266,
+        2.8121700286865234
+      ],
+      [
+        0.1309582144021988,
+        0.0019378525903448462,
+        0.20837600529193878,
+        176.01034545898438,
+        -9.392861102242023e-05,
+        89.99722290039062,
+        2.6752877235412598
+      ],
+      [
+        0.1308731883764267,
+        0.0018537212163209915,
+        0.2083592563867569,
+        176.01026916503906,
+        2.4707638658583164e-05,
+        89.99707794189453,
+        2.7936744689941406
+      ],
+      [
+        0.13076193630695343,
+        0.0019365751650184393,
+        0.20833690464496613,
+        176.0100555419922,
+        -0.0002790532889775932,
+        89.99759674072266,
+        2.7987899780273438
+      ],
+      [
+        0.13091255724430084,
+        0.001872793072834611,
+        0.20840522646903992,
+        176.010498046875,
+        -0.0003105513460468501,
+        89.9974365234375,
+        2.7447566986083984
+      ],
+      [
+        0.13093999028205872,
+        0.001893708249554038,
+        0.20825980603694916,
+        176.0099334716797,
+        -0.00030471800710074604,
+        89.99781799316406,
+        2.7550971508026123
+      ],
+      [
+        0.13093437254428864,
+        0.0019456628942862153,
+        0.20832644402980804,
+        176.01016235351562,
+        -0.00023496556968893856,
+        89.99756622314453,
+        2.7730345726013184
+      ],
+      [
+        0.13092423975467682,
+        0.0020397615153342485,
+        0.2084275335073471,
+        176.01026916503906,
+        -0.0002566525945439935,
+        89.9975357055664,
+        2.5209200382232666
+      ],
+      [
+        0.13100875914096832,
+        0.0019944070372730494,
+        0.2083629071712494,
+        176.01034545898438,
+        -0.0001990045275306329,
+        89.99735260009766,
+        2.650731325149536
+      ],
+      [
+        0.13095593452453613,
+        0.002013490069657564,
+        0.2083655595779419,
+        176.01016235351562,
+        -0.00017864975961856544,
+        89.9977035522461,
+        2.569013833999634
+      ],
+      [
+        0.13101328909397125,
+        0.0019990126602351665,
+        0.20823514461517334,
+        176.01010131835938,
+        -0.00026424636598676443,
+        89.99768829345703,
+        2.4885218143463135
+      ],
+      [
+        0.13076332211494446,
+        0.0019358580466359854,
+        0.20832638442516327,
+        176.00991821289062,
+        -0.000250067503657192,
+        89.997802734375,
+        2.43973708152771
+      ],
+      [
+        0.13072161376476288,
+        0.0019465481163933873,
+        0.2083868682384491,
+        176.01014709472656,
+        -0.0002342328371014446,
+        89.99793243408203,
+        2.4526679515838623
+      ],
+      [
+        0.1308751255273819,
+        0.0019334263633936644,
+        0.20822517573833466,
+        176.00994873046875,
+        7.753107638563961e-05,
+        89.99749755859375,
+        2.44692325592041
+      ],
+      [
+        0.13067835569381714,
+        0.0019546665716916323,
+        0.20831240713596344,
+        176.01031494140625,
+        2.4184257199522108e-05,
+        89.99747467041016,
+        2.4973621368408203
+      ]
+    ],
+    [
+      [
+        0.13090649247169495,
+        0.001984028145670891,
+        0.2085830420255661,
+        176.0093536376953,
+        9.991625847760588e-05,
+        89.99685668945312,
+        1.941438913345337
+      ],
+      [
+        0.13102982938289642,
+        0.0018029181519523263,
+        0.20834685862064362,
+        176.0104522705078,
+        -4.0416514821117744e-05,
+        89.99724578857422,
+        1.6200621128082275
+      ],
+      [
+        0.13095532357692719,
+        0.001849398948252201,
+        0.20848429203033447,
+        176.00917053222656,
+        -6.39401187072508e-05,
+        89.99720764160156,
+        1.4195177555084229
+      ],
+      [
+        0.13094648718833923,
+        0.0017738454043865204,
+        0.208510622382164,
+        176.009521484375,
+        0.00015144552162382752,
+        89.99703979492188,
+        1.3772574663162231
+      ],
+      [
+        0.13099123537540436,
+        0.0018878227565437555,
+        0.20841747522354126,
+        176.00897216796875,
+        -3.019630821654573e-05,
+        89.99641418457031,
+        1.4022036790847778
+      ],
+      [
+        0.13093942403793335,
+        0.0018786005675792694,
+        0.20844319462776184,
+        176.00914001464844,
+        -0.00021176200243644416,
+        89.99645233154297,
+        1.614084005355835
+      ],
+      [
+        0.13120214641094208,
+        0.002075829776003957,
+        0.20798411965370178,
+        176.01113891601562,
+        -0.00014000169176142663,
+        89.99726104736328,
+        1.4516884088516235
+      ],
+      [
+        0.1308683156967163,
+        0.0017220252193510532,
+        0.20816221833229065,
+        176.0096435546875,
+        0.00015574676217511296,
+        89.9964599609375,
+        1.5608067512512207
+      ],
+      [
+        0.1312122941017151,
+        0.0016933893784880638,
+        0.20803521573543549,
+        176.0125274658203,
+        0.0006242776289582253,
+        89.99784851074219,
+        1.6075034141540527
+      ],
+      [
+        0.13122178614139557,
+        0.0018606605008244514,
+        0.2081458568572998,
+        176.01385498046875,
+        0.0012434185482561588,
+        89.99808502197266,
+        1.813157558441162
+      ],
+      [
+        0.13137872517108917,
+        0.0017731842817738652,
+        0.20800799131393433,
+        176.01950073242188,
+        0.00288543663918972,
+        90.00159454345703,
+        1.6650338172912598
+      ],
+      [
+        0.13223792612552643,
+        0.0018986474024131894,
+        0.20766764879226685,
+        176.03329467773438,
+        0.005975669715553522,
+        90.0083236694336,
+        1.5360052585601807
+      ],
+      [
+        0.13343384861946106,
+        0.0024412712082266808,
+        0.20748776197433472,
+        176.0392608642578,
+        0.0074739959090948105,
+        90.01441192626953,
+        1.66856050491333
+      ],
+      [
+        0.13599742949008942,
+        0.0029446028638631105,
+        0.20740990340709686,
+        176.04579162597656,
+        0.008425360545516014,
+        90.01947021484375,
+        1.485578179359436
+      ],
+      [
+        0.13709905743598938,
+        0.0027356415521353483,
+        0.20741921663284302,
+        176.04534912109375,
+        0.00825597532093525,
+        90.02092742919922,
+        1.7326006889343262
+      ],
+      [
+        0.1390867829322815,
+        0.0033203703351318836,
+        0.20680299401283264,
+        176.04812622070312,
+        0.008466346189379692,
+        90.02267456054688,
+        1.9710044860839844
+      ]
+    ],
+    [
+      [
+        0.1310153752565384,
+        0.0020484186243265867,
+        0.20867235958576202,
+        176.0103759765625,
+        7.498337072320282e-05,
+        89.9969482421875,
+        2.07706880569458
+      ],
+      [
+        0.13092337548732758,
+        0.0018737518694251776,
+        0.20856735110282898,
+        176.010009765625,
+        0.00011865239503094926,
+        89.99681091308594,
+        1.917245864868164
+      ],
+      [
+        0.130991593003273,
+        0.00198960630223155,
+        0.2085607498884201,
+        176.01026916503906,
+        0.00019225882715545595,
+        89.99650573730469,
+        1.9162652492523193
+      ],
+      [
+        0.1310150921344757,
+        0.0018965776544064283,
+        0.208579882979393,
+        176.0106201171875,
+        -0.00027957410202361643,
+        89.99689483642578,
+        1.9838823080062866
+      ],
+      [
+        0.13119368255138397,
+        0.0018855177331715822,
+        0.20855091512203217,
+        176.010986328125,
+        -0.0001178017191705294,
+        89.99691772460938,
+        1.8703395128250122
+      ],
+      [
+        0.13115344941616058,
+        0.0018607029924169183,
+        0.20874591171741486,
+        176.01052856445312,
+        0.00029900955269113183,
+        89.996826171875,
+        1.8201812505722046
+      ],
+      [
+        0.13113023340702057,
+        0.001892806962132454,
+        0.20860904455184937,
+        176.0105743408203,
+        8.683081978233531e-05,
+        89.99661254882812,
+        2.000239372253418
+      ],
+      [
+        0.13118015229701996,
+        0.0017588331829756498,
+        0.2086266428232193,
+        176.010498046875,
+        -2.1451996872201562e-05,
+        89.99728393554688,
+        1.9351180791854858
+      ],
+      [
+        0.1312001347541809,
+        0.0018659919733181596,
+        0.20840662717819214,
+        176.01028442382812,
+        0.00010130468581337482,
+        89.99685668945312,
+        1.8629496097564697
+      ],
+      [
+        0.1310989409685135,
+        0.0015936631243675947,
+        0.2084645926952362,
+        176.010498046875,
+        0.0006172824068926275,
+        89.9972915649414,
+        2.1598763465881348
+      ],
+      [
+        0.13108214735984802,
+        0.0018113537225872278,
+        0.2086448222398758,
+        176.01040649414062,
+        0.0003828266926575452,
+        89.99705505371094,
+        2.1688787937164307
+      ],
+      [
+        0.1310548633337021,
+        0.0018182842759415507,
+        0.20859386026859283,
+        176.00999450683594,
+        0.0004258961998857558,
+        89.99716186523438,
+        2.1696231365203857
+      ],
+      [
+        0.1311027705669403,
+        0.001982384128496051,
+        0.20858445763587952,
+        176.01034545898438,
+        0.00016298754781018943,
+        89.99734497070312,
+        1.8949785232543945
+      ],
+      [
+        0.13124389946460724,
+        0.0017660831799730659,
+        0.20863954722881317,
+        176.01083374023438,
+        0.000130452259327285,
+        89.99725341796875,
+        1.879557728767395
+      ],
+      [
+        0.13118857145309448,
+        0.0017727112863212824,
+        0.20860184729099274,
+        176.01019287109375,
+        0.0004007643728982657,
+        89.99736785888672,
+        1.8223551511764526
+      ],
+      [
+        0.13120853900909424,
+        0.0015657893382012844,
+        0.20854398608207703,
+        176.0104522705078,
+        0.00038481553201563656,
+        89.99765014648438,
+        1.910325527191162
+      ]
+    ],
+    [
+      [
+        0.13114196062088013,
+        0.0021104831248521805,
+        0.20861603319644928,
+        176.01026916503906,
+        0.0004441899945959449,
+        89.99708557128906,
+        1.6497399806976318
+      ],
+      [
+        0.13090406358242035,
+        0.002218476962298155,
+        0.20861291885375977,
+        176.01036071777344,
+        0.0002968629414681345,
+        89.99708557128906,
+        1.4398622512817383
+      ],
+      [
+        0.1310281753540039,
+        0.002308339811861515,
+        0.2088124006986618,
+        176.0106964111328,
+        0.00045930143096484244,
+        89.99696350097656,
+        1.4085657596588135
+      ],
+      [
+        0.13102607429027557,
+        0.002101524267345667,
+        0.20869089663028717,
+        176.0106964111328,
+        0.00022399875160772353,
+        89.99706268310547,
+        1.4397765398025513
+      ],
+      [
+        0.13104389607906342,
+        0.0021394945215433836,
+        0.20860609412193298,
+        176.01100158691406,
+        0.000244353519519791,
+        89.99737548828125,
+        1.3889714479446411
+      ],
+      [
+        0.131087988615036,
+        0.002020082203671336,
+        0.20863638818264008,
+        176.01051330566406,
+        0.0002665068313945085,
+        89.99739074707031,
+        1.5445796251296997
+      ],
+      [
+        0.13116620481014252,
+        0.0020494465716183186,
+        0.20854100584983826,
+        176.01097106933594,
+        0.00017326883971691132,
+        89.99750518798828,
+        1.3476030826568604
+      ],
+      [
+        0.13109587132930756,
+        0.001901152078062296,
+        0.20862379670143127,
+        176.01075744628906,
+        0.0002977764524985105,
+        89.9974594116211,
+        1.5078661441802979
+      ],
+      [
+        0.13117161393165588,
+        0.0020071063190698624,
+        0.20859237015247345,
+        176.01097106933594,
+        5.3519212087849155e-05,
+        89.99737548828125,
+        1.649208426475525
+      ],
+      [
+        0.131106436252594,
+        0.0020802058279514313,
+        0.20880235731601715,
+        176.01036071777344,
+        -3.4542055800557137e-05,
+        89.9973373413086,
+        1.4160678386688232
+      ],
+      [
+        0.13109137117862701,
+        0.0021367210429161787,
+        0.2086499035358429,
+        176.0106658935547,
+        0.0002825793926604092,
+        89.99723815917969,
+        1.4032492637634277
+      ],
+      [
+        0.13108032941818237,
+        0.0020987510215491056,
+        0.20869462192058563,
+        176.01048278808594,
+        8.914720092434436e-05,
+        89.99732208251953,
+        1.4274038076400757
+      ],
+      [
+        0.1310896873474121,
+        0.001965466421097517,
+        0.2085879147052765,
+        176.0106201171875,
+        0.00011336547322571278,
+        89.99744415283203,
+        1.4202443361282349
+      ],
+      [
+        0.1309545487165451,
+        0.0020008983556181192,
+        0.20851023495197296,
+        176.01084899902344,
+        8.181034354493022e-05,
+        89.99724578857422,
+        1.3181438446044922
+      ],
+      [
+        0.13098056614398956,
+        0.0020052797626703978,
+        0.20866644382476807,
+        176.01084899902344,
+        1.581673313921783e-05,
+        89.99732208251953,
+        1.4290106296539307
+      ],
+      [
+        0.13093963265419006,
+        0.0019918722100555897,
+        0.20861992239952087,
+        176.01107788085938,
+        0.000114964168460574,
+        89.9972152709961,
+        1.5534965991973877
+      ]
+    ],
+    [
+      [
+        0.1310625970363617,
+        0.002262422349303961,
+        0.2089119255542755,
+        176.0103759765625,
+        0.0006257580826058984,
+        89.99360656738281,
+        1.7586634159088135
+      ],
+      [
+        0.13103747367858887,
+        0.0023066673893481493,
+        0.2088727205991745,
+        176.01119995117188,
+        0.0007684793672524393,
+        89.99567413330078,
+        1.7465505599975586
+      ],
+      [
+        0.13107457756996155,
+        0.0023887536954134703,
+        0.20881468057632446,
+        176.01182556152344,
+        0.0013535339385271072,
+        90.0029525756836,
+        1.772923469543457
+      ],
+      [
+        0.13104654848575592,
+        0.002411512192338705,
+        0.20869652926921844,
+        176.01312255859375,
+        0.0031110860873013735,
+        90.01178741455078,
+        1.9708569049835205
+      ],
+      [
+        0.131149023771286,
+        0.002343925880268216,
+        0.2086239606142044,
+        176.02142333984375,
+        0.010187304578721523,
+        90.04408264160156,
+        1.754522442817688
+      ],
+      [
+        0.13132363557815552,
+        0.0024899793788790703,
+        0.20870542526245117,
+        176.02398681640625,
+        0.013792487792670727,
+        90.0602035522461,
+        1.5829174518585205
+      ],
+      [
+        0.1315920054912567,
+        0.002792557468637824,
+        0.2087075561285019,
+        176.0263671875,
+        0.014749342575669289,
+        90.06623077392578,
+        1.6810896396636963
+      ],
+      [
+        0.13190563023090363,
+        0.002925220178440213,
+        0.20856058597564697,
+        176.02403259277344,
+        0.015008483082056046,
+        90.06809997558594,
+        1.7869353294372559
+      ],
+      [
+        0.13277889788150787,
+        0.0038367649540305138,
+        0.2084805965423584,
+        176.0255584716797,
+        0.014311396516859531,
+        90.07722473144531,
+        1.8236664533615112
+      ],
+      [
+        0.1340513527393341,
+        0.00496424688026309,
+        0.20844902098178864,
+        176.03277587890625,
+        0.014929043129086494,
+        90.08575439453125,
+        1.725683331489563
+      ],
+      [
+        0.13574852049350739,
+        0.00646184803918004,
+        0.20839162170886993,
+        176.0340576171875,
+        0.016113463789224625,
+        90.09547424316406,
+        1.8272876739501953
+      ],
+      [
+        0.13738156855106354,
+        0.00808830838650465,
+        0.20838771760463715,
+        176.0395050048828,
+        0.017198672518134117,
+        90.10438537597656,
+        1.5754625797271729
+      ],
+      [
+        0.13892367482185364,
+        0.0097963223233819,
+        0.20848999917507172,
+        176.0406036376953,
+        0.01659032143652439,
+        90.10819244384766,
+        1.5770574808120728
+      ],
+      [
+        0.1403716802597046,
+        0.011274179443717003,
+        0.20826447010040283,
+        176.04229736328125,
+        0.01705697737634182,
+        90.115966796875,
+        1.64292311668396
+      ],
+      [
+        0.1418147087097168,
+        0.013074633665382862,
+        0.20814748108386993,
+        176.04641723632812,
+        0.016927046701312065,
+        90.1190414428711,
+        1.632869005203247
+      ],
+      [
+        0.1428920179605484,
+        0.0144652109593153,
+        0.20800454914569855,
+        176.04747009277344,
+        0.018901916220784187,
+        90.12922668457031,
+        1.4252182245254517
+      ]
+    ],
+    [
+      [
+        0.13105656206607819,
+        0.0023602405562996864,
+        0.20898427069187164,
+        176.01063537597656,
+        0.0006020718137733638,
+        89.9943618774414,
+        2.179922342300415
+      ],
+      [
+        0.13098596036434174,
+        0.002325172768905759,
+        0.20916786789894104,
+        176.01051330566406,
+        0.0004310974618420005,
+        89.99432373046875,
+        2.1195032596588135
+      ],
+      [
+        0.1309630572795868,
+        0.0022223670966923237,
+        0.20889264345169067,
+        176.01052856445312,
+        0.0006308387964963913,
+        89.99467468261719,
+        2.014186382293701
+      ],
+      [
+        0.13118304312229156,
+        0.002115617273375392,
+        0.20892487466335297,
+        176.01077270507812,
+        0.00036812995676882565,
+        89.99479675292969,
+        1.9647281169891357
+      ],
+      [
+        0.13123923540115356,
+        0.002276036189869046,
+        0.20901867747306824,
+        176.01104736328125,
+        0.0006821491406299174,
+        89.99464416503906,
+        2.115300416946411
+      ],
+      [
+        0.1309644877910614,
+        0.0024242354556918144,
+        0.20889374613761902,
+        176.01104736328125,
+        0.0007410533144138753,
+        89.99494934082031,
+        2.092991590499878
+      ],
+      [
+        0.1310899704694748,
+        0.0024881968274712563,
+        0.2086620181798935,
+        176.01051330566406,
+        0.0006960235186852515,
+        89.99518585205078,
+        2.0279088020324707
+      ],
+      [
+        0.13100241124629974,
+        0.0023330613039433956,
+        0.20857368409633636,
+        176.01121520996094,
+        0.0011248154332861304,
+        89.99490356445312,
+        2.076280117034912
+      ],
+      [
+        0.13133551180362701,
+        0.002074543386697769,
+        0.20882956683635712,
+        176.0106201171875,
+        0.0007992343162186444,
+        89.99488830566406,
+        2.3357579708099365
+      ],
+      [
+        0.13142457604408264,
+        0.0023106110747903585,
+        0.2090350091457367,
+        176.01181030273438,
+        0.0017663476755842566,
+        89.9958724975586,
+        2.1653404235839844
+      ],
+      [
+        0.13143305480480194,
+        0.0022212073672562838,
+        0.2087664157152176,
+        176.0148162841797,
+        0.004349719267338514,
+        90.00025177001953,
+        2.082001209259033
+      ],
+      [
+        0.13188883662223816,
+        0.0025496985763311386,
+        0.20867471396923065,
+        176.02198791503906,
+        0.008892725221812725,
+        90.01324462890625,
+        2.2912440299987793
+      ],
+      [
+        0.13280987739562988,
+        0.0028099198825657368,
+        0.2085939645767212,
+        176.0329132080078,
+        0.015903787687420845,
+        90.03829956054688,
+        2.3173186779022217
+      ],
+      [
+        0.134368896484375,
+        0.0036931594368070364,
+        0.2086687684059143,
+        176.04147338867188,
+        0.019611438736319542,
+        90.05754089355469,
+        2.2287545204162598
+      ],
+      [
+        0.13569042086601257,
+        0.004773235879838467,
+        0.20859681069850922,
+        176.0457000732422,
+        0.019823826849460602,
+        90.0657958984375,
+        2.272202253341675
+      ],
+      [
+        0.1372840255498886,
+        0.005555036943405867,
+        0.20822647213935852,
+        176.04730224609375,
+        0.019732844084501266,
+        90.06982421875,
+        2.210867404937744
+      ]
+    ],
+    [
+      [
+        0.1311476230621338,
+        0.0023023055400699377,
+        0.20911815762519836,
+        176.01226806640625,
+        0.00027950218645855784,
+        89.9934310913086,
+        1.5571088790893555
+      ],
+      [
+        0.13097205758094788,
+        0.0022247517481446266,
+        0.20913486182689667,
+        176.0115203857422,
+        0.0001242578000528738,
+        89.99359130859375,
+        1.5539366006851196
+      ],
+      [
+        0.13105258345603943,
+        0.0022721458226442337,
+        0.20916083455085754,
+        176.0118865966797,
+        0.00018107796495314687,
+        89.99371337890625,
+        1.5149725675582886
+      ],
+      [
+        0.1310078203678131,
+        0.0023006191477179527,
+        0.20906765758991241,
+        176.01211547851562,
+        -4.122857262700563e-06,
+        89.99373626708984,
+        1.4572826623916626
+      ],
+      [
+        0.1312715858221054,
+        0.0022105432581156492,
+        0.20926624536514282,
+        176.01239013671875,
+        -5.533808871405199e-05,
+        89.99380493164062,
+        1.5611789226531982
+      ],
+      [
+        0.13128021359443665,
+        0.002131375949829817,
+        0.2090412974357605,
+        176.011962890625,
+        3.664378164103255e-05,
+        89.99382019042969,
+        1.5480589866638184
+      ],
+      [
+        0.1313270628452301,
+        0.0021946816705167294,
+        0.20888619124889374,
+        176.01206970214844,
+        8.832529420033097e-05,
+        89.9942626953125,
+        1.4475888013839722
+      ],
+      [
+        0.13130341470241547,
+        0.0023487750440835953,
+        0.2091445028781891,
+        176.0122528076172,
+        0.0001613702770555392,
+        89.99378204345703,
+        1.4258266687393188
+      ],
+      [
+        0.1312701255083084,
+        0.0021396568045020103,
+        0.20899201929569244,
+        176.01220703125,
+        0.00018853852816391736,
+        89.99388885498047,
+        1.495673656463623
+      ],
+      [
+        0.13117635250091553,
+        0.0022341476287692785,
+        0.20899060368537903,
+        176.0117950439453,
+        -0.0002515203959774226,
+        89.9941635131836,
+        1.4690793752670288
+      ],
+      [
+        0.13114149868488312,
+        0.0022697364911437035,
+        0.20916904509067535,
+        176.0121307373047,
+        0.00020011000742670149,
+        89.99385833740234,
+        1.5265271663665771
+      ],
+      [
+        0.13123348355293274,
+        0.0022597748320549726,
+        0.20883791148662567,
+        176.01217651367188,
+        -7.763412577332929e-05,
+        89.99383544921875,
+        1.411619782447815
+      ],
+      [
+        0.13118508458137512,
+        0.002116534160450101,
+        0.20905648171901703,
+        176.01190185546875,
+        8.678370068082586e-05,
+        89.99382781982422,
+        1.3278228044509888
+      ],
+      [
+        0.13125251233577728,
+        0.0021482682786881924,
+        0.2087644338607788,
+        176.01190185546875,
+        0.000553049729205668,
+        89.99381256103516,
+        1.415297031402588
+      ],
+      [
+        0.1312207579612732,
+        0.00210443208925426,
+        0.20895741879940033,
+        176.0117950439453,
+        1.1949703548452817e-05,
+        89.99390411376953,
+        1.4966896772384644
+      ],
+      [
+        0.13124097883701324,
+        0.002255511237308383,
+        0.20898395776748657,
+        176.01194763183594,
+        0.0004619814280886203,
+        89.99407958984375,
+        1.5609248876571655
+      ]
+    ],
+    [
+      [
+        0.13151052594184875,
+        0.002042755950242281,
+        0.20936307311058044,
+        176.01976013183594,
+        0.00022363767493516207,
+        89.9924545288086,
+        1.3097466230392456
+      ],
+      [
+        0.13233833014965057,
+        0.0023028762079775333,
+        0.20960733294487,
+        176.0278778076172,
+        0.0008716406882740557,
+        89.99310302734375,
+        1.2867172956466675
+      ],
+      [
+        0.13352639973163605,
+        0.0022889645770192146,
+        0.20966385304927826,
+        176.03616333007812,
+        0.001322271884419024,
+        89.99298095703125,
+        0.9050455689430237
+      ],
+      [
+        0.13523758947849274,
+        0.002218940993770957,
+        0.20954017341136932,
+        176.04820251464844,
+        0.0014007886638864875,
+        89.99150085449219,
+        0.9168777465820312
+      ],
+      [
+        0.1372406929731369,
+        0.002466500038281083,
+        0.20978009700775146,
+        176.0536346435547,
+        0.0012290149461477995,
+        89.99015045166016,
+        0.8074049949645996
+      ],
+      [
+        0.13988526165485382,
+        0.002740683266893029,
+        0.2097521722316742,
+        176.06417846679688,
+        0.0014923993730917573,
+        89.99031829833984,
+        0.8328002095222473
+      ],
+      [
+        0.14248020946979523,
+        0.002650332869961858,
+        0.20973479747772217,
+        176.06959533691406,
+        0.0038100075908005238,
+        89.99609375,
+        0.8229233026504517
+      ],
+      [
+        0.14521433413028717,
+        0.0031598724890500307,
+        0.20975175499916077,
+        176.06761169433594,
+        0.00578471552580595,
+        90.00577545166016,
+        1.0089300870895386
+      ],
+      [
+        0.14832162857055664,
+        0.003720385953783989,
+        0.20975050330162048,
+        176.07077026367188,
+        0.008568760938942432,
+        90.02093505859375,
+        1.0272070169448853
+      ],
+      [
+        0.1511543095111847,
+        0.004450344480574131,
+        0.20981034636497498,
+        176.07644653320312,
+        0.00839082058519125,
+        90.02198028564453,
+        1.0196782350540161
+      ],
+      [
+        0.15441060066223145,
+        0.005427340976893902,
+        0.2099592387676239,
+        176.077880859375,
+        0.007006715517491102,
+        90.02384185791016,
+        1.1112313270568848
+      ],
+      [
+        0.15761025249958038,
+        0.00667285080999136,
+        0.2099599987268448,
+        176.08236694335938,
+        0.00703786127269268,
+        90.03225708007812,
+        0.9614713788032532
+      ],
+      [
+        0.1605365127325058,
+        0.0077243587002158165,
+        0.20988596975803375,
+        176.08262634277344,
+        0.006635352503508329,
+        90.03717803955078,
+        0.8564586043357849
+      ],
+      [
+        0.16319555044174194,
+        0.009182668291032314,
+        0.2097242921590805,
+        176.0826416015625,
+        0.006157096475362778,
+        90.04132843017578,
+        1.0396623611450195
+      ],
+      [
+        0.16594825685024261,
+        0.010410971939563751,
+        0.20984859764575958,
+        176.0836639404297,
+        0.005508731584995985,
+        90.04381561279297,
+        1.0547109842300415
+      ],
+      [
+        0.1688094139099121,
+        0.011989920400083065,
+        0.21000660955905914,
+        176.0830078125,
+        0.004602197557687759,
+        90.04736328125,
+        1.071629285812378
+      ]
+    ],
+    [
+      [
+        0.1314568668603897,
+        0.0019841394387185574,
+        0.20942673087120056,
+        176.0360870361328,
+        0.0015758902300149202,
+        89.99384307861328,
+        1.5428340435028076
+      ],
+      [
+        0.13215768337249756,
+        0.0020423580426722765,
+        0.20930837094783783,
+        176.0417022705078,
+        0.002681471174582839,
+        89.99696350097656,
+        1.2514009475708008
+      ],
+      [
+        0.1331777721643448,
+        0.0021136198192834854,
+        0.20938844978809357,
+        176.0500946044922,
+        0.0039357589557766914,
+        90.00202941894531,
+        1.419399619102478
+      ],
+      [
+        0.13547347486019135,
+        0.002146233571693301,
+        0.209134042263031,
+        176.05746459960938,
+        0.006314098369330168,
+        90.01226043701172,
+        1.4029184579849243
+      ],
+      [
+        0.1375972330570221,
+        0.0024197783786803484,
+        0.20914362370967865,
+        176.05682373046875,
+        0.0061169457621872425,
+        90.01567840576172,
+        1.5387314558029175
+      ],
+      [
+        0.14092472195625305,
+        0.0028871288523077965,
+        0.20905987918376923,
+        176.07350158691406,
+        0.011073993518948555,
+        90.03189849853516,
+        1.313731074333191
+      ],
+      [
+        0.1425974816083908,
+        0.00293346936814487,
+        0.209095761179924,
+        176.07315063476562,
+        0.010652842931449413,
+        90.03335571289062,
+        1.2736269235610962
+      ],
+      [
+        0.1451222002506256,
+        0.003264100756496191,
+        0.20909281075000763,
+        176.0758514404297,
+        0.009987112134695053,
+        90.03465270996094,
+        1.4777215719223022
+      ],
+      [
+        0.14776135981082916,
+        0.0036091539077460766,
+        0.20909354090690613,
+        176.0794219970703,
+        0.010137313045561314,
+        90.03825378417969,
+        1.5821022987365723
+      ],
+      [
+        0.15159934759140015,
+        0.004544712137430906,
+        0.20896752178668976,
+        176.08078002929688,
+        0.010858617722988129,
+        90.04562377929688,
+        1.7882821559906006
+      ],
+      [
+        0.15516579151153564,
+        0.0051415711641311646,
+        0.20919169485569,
+        176.08465576171875,
+        0.010859445668756962,
+        90.0510025024414,
+        1.5783836841583252
+      ],
+      [
+        0.15876927971839905,
+        0.005913090892136097,
+        0.20912276208400726,
+        176.0886993408203,
+        0.011329889297485352,
+        90.05593872070312,
+        1.41685950756073
+      ],
+      [
+        0.16144254803657532,
+        0.00663384422659874,
+        0.20931585133075714,
+        176.089111328125,
+        0.010171589441597462,
+        90.06047821044922,
+        1.4883959293365479
+      ],
+      [
+        0.16442343592643738,
+        0.007534189149737358,
+        0.209103524684906,
+        176.0906524658203,
+        0.009833579882979393,
+        90.0640640258789,
+        1.38670015335083
+      ],
+      [
+        0.16815185546875,
+        0.00848990585654974,
+        0.20915384590625763,
+        176.08929443359375,
+        0.008986596949398518,
+        90.06449127197266,
+        1.4265620708465576
+      ],
+      [
+        0.17166908085346222,
+        0.008982961997389793,
+        0.2090887427330017,
+        176.0904541015625,
+        0.00895396713167429,
+        90.06568908691406,
+        1.471843957901001
+      ]
+    ],
+    [
+      [
+        0.13194192945957184,
+        0.0020381570793688297,
+        0.20943905413150787,
+        176.05006408691406,
+        0.0034663670230656862,
+        89.99756622314453,
+        1.0901957750320435
+      ],
+      [
+        0.13351790606975555,
+        0.0022004081401973963,
+        0.20926451683044434,
+        176.05735778808594,
+        0.0038050992880016565,
+        90.00048065185547,
+        1.0187803506851196
+      ],
+      [
+        0.1356489509344101,
+        0.0023300061002373695,
+        0.20923949778079987,
+        176.0625762939453,
+        0.0044997213408350945,
+        90.00433349609375,
+        1.0298209190368652
+      ],
+      [
+        0.13781221210956573,
+        0.0023140942212194204,
+        0.20946456491947174,
+        176.06361389160156,
+        0.00490722618997097,
+        90.00704193115234,
+        1.1209251880645752
+      ],
+      [
+        0.13995090126991272,
+        0.0025345468893647194,
+        0.2088715136051178,
+        176.06448364257812,
+        0.005834353156387806,
+        90.0121078491211,
+        1.0788302421569824
+      ],
+      [
+        0.14341044425964355,
+        0.0028224962297827005,
+        0.2091182917356491,
+        176.06651306152344,
+        0.006371770519763231,
+        90.02145385742188,
+        0.8218777179718018
+      ],
+      [
+        0.14526797831058502,
+        0.003030006540939212,
+        0.2089725136756897,
+        176.0680694580078,
+        0.005698702298104763,
+        90.01937103271484,
+        0.8076294660568237
+      ],
+      [
+        0.14800581336021423,
+        0.00334921944886446,
+        0.20885704457759857,
+        176.068115234375,
+        0.006393552757799625,
+        90.02450561523438,
+        1.0107996463775635
+      ],
+      [
+        0.15192146599292755,
+        0.0039702826179564,
+        0.20883382856845856,
+        176.06698608398438,
+        0.006974372547119856,
+        90.03755187988281,
+        0.9997147917747498
+      ],
+      [
+        0.15492726862430573,
+        0.004420331679284573,
+        0.2086879462003708,
+        176.0714111328125,
+        0.006185361184179783,
+        90.0416488647461,
+        0.9014982581138611
+      ],
+      [
+        0.15756529569625854,
+        0.004949979484081268,
+        0.2086573988199234,
+        176.06944274902344,
+        0.0053632911294698715,
+        90.04232788085938,
+        1.0403151512145996
+      ],
+      [
+        0.1608741730451584,
+        0.005672391038388014,
+        0.20874115824699402,
+        176.0690460205078,
+        0.005566648207604885,
+        90.04853820800781,
+        0.9512991309165955
+      ],
+      [
+        0.1636040210723877,
+        0.006388437934219837,
+        0.20864614844322205,
+        176.0677490234375,
+        0.005087478552013636,
+        90.05046081542969,
+        0.9253427982330322
+      ],
+      [
+        0.16760578751564026,
+        0.008203347213566303,
+        0.20850533246994019,
+        176.06924438476562,
+        0.0047338251024484634,
+        90.06475067138672,
+        0.7220692038536072
+      ],
+      [
+        0.17042122781276703,
+        0.009444565512239933,
+        0.2084595412015915,
+        176.0691680908203,
+        0.0033530883956700563,
+        90.07066345214844,
+        0.7836520671844482
+      ],
+      [
+        0.17292018234729767,
+        0.010384578257799149,
+        0.208389550447464,
+        176.06756591796875,
+        0.0035885432735085487,
+        90.07406616210938,
+        0.9000303149223328
+      ]
+    ],
+    [
+      [
+        0.13395386934280396,
+        0.0022533698938786983,
+        0.2094932496547699,
+        176.0591278076172,
+        0.00533080380409956,
+        90.00312805175781,
+        1.1050819158554077
+      ],
+      [
+        0.1360653042793274,
+        0.002572509925812483,
+        0.20953978598117828,
+        176.06268310546875,
+        0.005043677054345608,
+        90.0066146850586,
+        1.2796581983566284
+      ],
+      [
+        0.13883723318576813,
+        0.0025573207531124353,
+        0.20933352410793304,
+        176.06472778320312,
+        0.005874339956790209,
+        90.00873565673828,
+        0.9890167713165283
+      ],
+      [
+        0.1411985158920288,
+        0.002616009907796979,
+        0.2094775289297104,
+        176.06710815429688,
+        0.005981623660773039,
+        90.0102310180664,
+        1.0719985961914062
+      ],
+      [
+        0.14446327090263367,
+        0.0030615264549851418,
+        0.209388867020607,
+        176.06854248046875,
+        0.007215661462396383,
+        90.01829528808594,
+        1.1514861583709717
+      ],
+      [
+        0.14741630852222443,
+        0.003342954209074378,
+        0.20955635607242584,
+        176.06753540039062,
+        0.007192699238657951,
+        90.02474975585938,
+        1.1164416074752808
+      ],
+      [
+        0.15000857412815094,
+        0.0037814953830093145,
+        0.20945404469966888,
+        176.06771850585938,
+        0.006663817912340164,
+        90.02511596679688,
+        0.9878707528114319
+      ],
+      [
+        0.1527121216058731,
+        0.004268007352948189,
+        0.2095233052968979,
+        176.0683135986328,
+        0.006809584330767393,
+        90.02764129638672,
+        1.0467628240585327
+      ],
+      [
+        0.15771082043647766,
+        0.005399511661380529,
+        0.20935118198394775,
+        176.0680694580078,
+        0.0070977103896439075,
+        90.03895568847656,
+        0.9624401926994324
+      ],
+      [
+        0.1604139506816864,
+        0.006183587480336428,
+        0.20949584245681763,
+        176.06729125976562,
+        0.007079924922436476,
+        90.04581451416016,
+        1.0410269498825073
+      ],
+      [
+        0.16385667026042938,
+        0.007031232584267855,
+        0.20934681594371796,
+        176.0692901611328,
+        0.006450592074543238,
+        90.04928588867188,
+        1.1203993558883667
+      ],
+      [
+        0.16690054535865784,
+        0.007950100116431713,
+        0.20940165221691132,
+        176.0672149658203,
+        0.005291949957609177,
+        90.05448150634766,
+        1.1258074045181274
+      ],
+      [
+        0.1715908795595169,
+        0.009461349807679653,
+        0.20933300256729126,
+        176.06492614746094,
+        0.005738032516092062,
+        90.06134033203125,
+        1.0616520643234253
+      ],
+      [
+        0.1746007353067398,
+        0.010603931732475758,
+        0.20925778150558472,
+        176.06610107421875,
+        0.006280864588916302,
+        90.0672836303711,
+        0.9891526699066162
+      ],
+      [
+        0.17728064954280853,
+        0.011727586388587952,
+        0.2093227207660675,
+        176.06336975097656,
+        0.0034990361891686916,
+        90.06891632080078,
+        1.1245964765548706
+      ],
+      [
+        0.1798211634159088,
+        0.013041124679148197,
+        0.2089427411556244,
+        176.0674591064453,
+        0.005642501171678305,
+        90.07917785644531,
+        0.9584941267967224
+      ]
+    ],
+    [
+      [
+        0.13620370626449585,
+        0.0022139614447951317,
+        0.20934607088565826,
+        176.06236267089844,
+        0.005754353944212198,
+        90.00737762451172,
+        0.46630996465682983
+      ],
+      [
+        0.13915377855300903,
+        0.002330706687644124,
+        0.20923729240894318,
+        176.06224060058594,
+        0.005820975638926029,
+        90.00996398925781,
+        0.4506203532218933
+      ],
+      [
+        0.14203476905822754,
+        0.0024031787179410458,
+        0.20937073230743408,
+        176.06167602539062,
+        0.005333679262548685,
+        90.01258087158203,
+        0.5760780572891235
+      ],
+      [
+        0.14495201408863068,
+        0.002607988426461816,
+        0.2094348967075348,
+        176.0612335205078,
+        0.005184772424399853,
+        90.01567077636719,
+        0.47035640478134155
+      ],
+      [
+        0.14804714918136597,
+        0.002673815470188856,
+        0.20937857031822205,
+        176.06309509277344,
+        0.004686256404966116,
+        90.01692962646484,
+        0.4908308684825897
+      ],
+      [
+        0.15090087056159973,
+        0.0030027381144464016,
+        0.20922842621803284,
+        176.0636444091797,
+        0.0049302950501441956,
+        90.01665496826172,
+        0.5288674831390381
+      ],
+      [
+        0.15503552556037903,
+        0.003138835309073329,
+        0.20920805633068085,
+        176.0633087158203,
+        0.0045907823368906975,
+        90.01863098144531,
+        0.370364785194397
+      ],
+      [
+        0.15798354148864746,
+        0.003427238669246435,
+        0.20912602543830872,
+        176.0625,
+        0.003743237815797329,
+        90.0203628540039,
+        0.5804671049118042
+      ],
+      [
+        0.16167676448822021,
+        0.0036886923480778933,
+        0.20919255912303925,
+        176.05999755859375,
+        0.002865498885512352,
+        90.02029418945312,
+        0.6309384703636169
+      ],
+      [
+        0.16514182090759277,
+        0.003945154137909412,
+        0.2091439813375473,
+        176.05795288085938,
+        0.0019318630220368505,
+        90.02252197265625,
+        0.27112337946891785
+      ],
+      [
+        0.1684022694826126,
+        0.004282447509467602,
+        0.20895610749721527,
+        176.05934143066406,
+        0.002354203024879098,
+        90.02371978759766,
+        0.3780146539211273
+      ],
+      [
+        0.1713954657316208,
+        0.004515091888606548,
+        0.2089526355266571,
+        176.05618286132812,
+        0.001018472365103662,
+        90.02404022216797,
+        0.4196428656578064
+      ],
+      [
+        0.1747693419456482,
+        0.004693181719630957,
+        0.20894064009189606,
+        176.0546112060547,
+        0.0001507062988821417,
+        90.02482604980469,
+        0.4931110441684723
+      ],
+      [
+        0.1781192272901535,
+        0.00513042276725173,
+        0.20895494520664215,
+        176.05323791503906,
+        0.0002644608321134001,
+        90.0260238647461,
+        0.54234778881073
+      ],
+      [
+        0.18171963095664978,
+        0.0055943503975868225,
+        0.2089567333459854,
+        176.0541534423828,
+        -0.0020066758152097464,
+        90.0269546508789,
+        0.6478036046028137
+      ],
+      [
+        0.18347924947738647,
+        0.005895908921957016,
+        0.2089943289756775,
+        176.05252075195312,
+        -0.0012818975374102592,
+        90.02947998046875,
+        0.7981542944908142
+      ]
+    ],
+    [
+      [
+        0.1415129005908966,
+        0.0027319975197315216,
+        0.2093348205089569,
+        176.06459045410156,
+        0.0035483501851558685,
+        90.00851440429688,
+        0.9952843189239502
+      ],
+      [
+        0.14463184773921967,
+        0.0034573955927044153,
+        0.2093559056520462,
+        176.0646209716797,
+        0.0034132797736674547,
+        90.01188659667969,
+        0.9798665046691895
+      ],
+      [
+        0.1475665122270584,
+        0.0038825850933790207,
+        0.2093151956796646,
+        176.06668090820312,
+        0.0023440546356141567,
+        90.0120620727539,
+        0.7271140217781067
+      ],
+      [
+        0.14940232038497925,
+        0.003983384929597378,
+        0.20927320420742035,
+        176.06834411621094,
+        0.00267407507635653,
+        90.01448822021484,
+        0.6987061500549316
+      ],
+      [
+        0.1526920348405838,
+        0.00442832987755537,
+        0.20909786224365234,
+        176.06927490234375,
+        0.0022413244005292654,
+        90.01576232910156,
+        0.7444576025009155
+      ],
+      [
+        0.15560398995876312,
+        0.004830278921872377,
+        0.20910058915615082,
+        176.06788635253906,
+        0.0025365115143358707,
+        90.0177230834961,
+        0.8044632077217102
+      ],
+      [
+        0.15908944606781006,
+        0.005355764180421829,
+        0.2089800238609314,
+        176.0677947998047,
+        0.0007235665107145905,
+        90.0152816772461,
+        0.7970201373100281
+      ],
+      [
+        0.16225340962409973,
+        0.005928054917603731,
+        0.20895995199680328,
+        176.06607055664062,
+        0.00022274779621511698,
+        90.01651763916016,
+        0.7135864496231079
+      ],
+      [
+        0.16526153683662415,
+        0.006400957703590393,
+        0.2089604288339615,
+        176.06787109375,
+        -1.0556516826909501e-05,
+        90.01866912841797,
+        0.8239924907684326
+      ],
+      [
+        0.16810795664787292,
+        0.006781307980418205,
+        0.20872263610363007,
+        176.06768798828125,
+        -0.000546993687748909,
+        90.01913452148438,
+        0.8681785464286804
+      ],
+      [
+        0.17112158238887787,
+        0.007493982091546059,
+        0.20866824686527252,
+        176.06712341308594,
+        -0.001327631063759327,
+        90.02007293701172,
+        0.7277992367744446
+      ],
+      [
+        0.17460274696350098,
+        0.008094617165625095,
+        0.20866800844669342,
+        176.0659942626953,
+        -0.0019740925636142492,
+        90.01825714111328,
+        0.7027584910392761
+      ],
+      [
+        0.17734290659427643,
+        0.008914713747799397,
+        0.20877674221992493,
+        176.0680694580078,
+        -0.0023505177814513445,
+        90.01923370361328,
+        0.4651167094707489
+      ],
+      [
+        0.180923730134964,
+        0.009290380403399467,
+        0.20865961909294128,
+        176.06640625,
+        -0.0033232741989195347,
+        90.01900482177734,
+        0.6333544850349426
+      ],
+      [
+        0.18475398421287537,
+        0.010008219629526138,
+        0.20842184126377106,
+        176.06101989746094,
+        -0.0037326866295188665,
+        90.01280975341797,
+        0.6913871169090271
+      ],
+      [
+        0.18697169423103333,
+        0.010109730996191502,
+        0.20837467908859253,
+        176.05958557128906,
+        -0.004359754268079996,
+        90.01374053955078,
+        0.8133594989776611
+      ]
+    ],
+    [
+      [
+        0.1459393948316574,
+        0.0026647618506103754,
+        0.2093164324760437,
+        176.06600952148438,
+        0.00192162801977247,
+        90.01443481445312,
+        0.44950979948043823
+      ],
+      [
+        0.14939002692699432,
+        0.0033358128275722265,
+        0.20907078683376312,
+        176.0657958984375,
+        0.0015133906854316592,
+        90.01676940917969,
+        0.5347806215286255
+      ],
+      [
+        0.15198354423046112,
+        0.003609301522374153,
+        0.20909734070301056,
+        176.0636444091797,
+        0.0013040334451943636,
+        90.01792907714844,
+        0.6040546894073486
+      ],
+      [
+        0.15443632006645203,
+        0.0040175761096179485,
+        0.2092650681734085,
+        176.06214904785156,
+        0.0008769400883466005,
+        90.01868438720703,
+        0.40932294726371765
+      ],
+      [
+        0.15729935467243195,
+        0.004496479872614145,
+        0.20915155112743378,
+        176.0602569580078,
+        0.0003366203745827079,
+        90.02132415771484,
+        0.43739408254623413
+      ],
+      [
+        0.16091039776802063,
+        0.004928280599415302,
+        0.20907168090343475,
+        176.05764770507812,
+        -0.00043829786591231823,
+        90.02033233642578,
+        0.3386193513870239
+      ],
+      [
+        0.16445761919021606,
+        0.0054289959371089935,
+        0.20917731523513794,
+        176.05711364746094,
+        -0.001454166485927999,
+        90.01973724365234,
+        0.5100647807121277
+      ],
+      [
+        0.1679045706987381,
+        0.005899304524064064,
+        0.20913070440292358,
+        176.054931640625,
+        -0.0021595845464617014,
+        90.02226257324219,
+        0.36865168809890747
+      ],
+      [
+        0.17089883983135223,
+        0.006172821391373873,
+        0.20922040939331055,
+        176.05357360839844,
+        -0.0021098870784044266,
+        90.02401733398438,
+        0.42084795236587524
+      ],
+      [
+        0.17408177256584167,
+        0.006712178699672222,
+        0.209229975938797,
+        176.05125427246094,
+        -0.002903751563280821,
+        90.02425384521484,
+        0.44427600502967834
+      ],
+      [
+        0.17689931392669678,
+        0.007295092102140188,
+        0.20918262004852295,
+        176.05206298828125,
+        -0.003444937290623784,
+        90.02523803710938,
+        0.45067352056503296
+      ],
+      [
+        0.18033607304096222,
+        0.007948377169668674,
+        0.20906803011894226,
+        176.04959106445312,
+        -0.004197859205305576,
+        90.02897644042969,
+        0.24019905924797058
+      ],
+      [
+        0.18311168253421783,
+        0.008662025444209576,
+        0.2091931700706482,
+        176.04855346679688,
+        -0.003918559290468693,
+        90.03141784667969,
+        0.3362092077732086
+      ],
+      [
+        0.1847749501466751,
+        0.009233217686414719,
+        0.20921775698661804,
+        176.05078125,
+        -0.00421652989462018,
+        90.03890228271484,
+        0.5818966627120972
+      ],
+      [
+        0.18705809116363525,
+        0.010348127223551273,
+        0.2091480791568756,
+        176.049072265625,
+        -0.004549405071884394,
+        90.04230499267578,
+        0.5968301296234131
+      ],
+      [
+        0.188578262925148,
+        0.0115588940680027,
+        0.20919278264045715,
+        176.0491943359375,
+        -0.005167542025446892,
+        90.04813385009766,
+        0.6264194250106812
+      ]
+    ],
+    [
+      [
+        0.14900004863739014,
+        0.002921953797340393,
+        0.20949354767799377,
+        176.06668090820312,
+        0.001516735297627747,
+        90.01408386230469,
+        0.3026089072227478
+      ],
+      [
+        0.15179364383220673,
+        0.003116740146651864,
+        0.20940186083316803,
+        176.0670623779297,
+        0.0011123995063826442,
+        90.015625,
+        0.5235627889633179
+      ],
+      [
+        0.15444277226924896,
+        0.0033007243182510138,
+        0.20949342846870422,
+        176.0649871826172,
+        0.0008960908744484186,
+        90.01746368408203,
+        0.3297939598560333
+      ],
+      [
+        0.15699930489063263,
+        0.003456363920122385,
+        0.209568053483963,
+        176.06341552734375,
+        0.0008366538095287979,
+        90.01636505126953,
+        0.49890604615211487
+      ],
+      [
+        0.16086389124393463,
+        0.004019460640847683,
+        0.20943576097488403,
+        176.063232421875,
+        5.518854231922887e-05,
+        90.01851654052734,
+        0.536245584487915
+      ],
+      [
+        0.1648116111755371,
+        0.004216460045427084,
+        0.20931807160377502,
+        176.06207275390625,
+        -0.0003157079336233437,
+        90.02035522460938,
+        0.4826611578464508
+      ],
+      [
+        0.16802358627319336,
+        0.004554403014481068,
+        0.2093571126461029,
+        176.0594482421875,
+        -0.0005549978232011199,
+        90.02200317382812,
+        0.5079677104949951
+      ],
+      [
+        0.17050562798976898,
+        0.00496663898229599,
+        0.20933926105499268,
+        176.0605926513672,
+        -0.0013579692458733916,
+        90.02130126953125,
+        0.49605876207351685
+      ],
+      [
+        0.17323371767997742,
+        0.005222120322287083,
+        0.20939065515995026,
+        176.0598602294922,
+        -0.0015922823222354054,
+        90.02249145507812,
+        0.5645294189453125
+      ],
+      [
+        0.1767740249633789,
+        0.005624893121421337,
+        0.2093580961227417,
+        176.0605010986328,
+        -0.0015111772809177637,
+        90.02519226074219,
+        0.40448492765426636
+      ],
+      [
+        0.17952607572078705,
+        0.005938116926699877,
+        0.20940861105918884,
+        176.0559844970703,
+        -0.0025071382988244295,
+        90.0236587524414,
+        0.4641360938549042
+      ],
+      [
+        0.18321995437145233,
+        0.006393213756382465,
+        0.20940688252449036,
+        176.05604553222656,
+        -0.002185596851631999,
+        90.02716064453125,
+        0.3673107624053955
+      ],
+      [
+        0.18606168031692505,
+        0.006786658428609371,
+        0.20944973826408386,
+        176.0542755126953,
+        -0.0034062264021486044,
+        90.02542114257812,
+        0.308982789516449
+      ],
+      [
+        0.18914251029491425,
+        0.007327276282012463,
+        0.20951667428016663,
+        176.05125427246094,
+        -0.0028737098909914494,
+        90.02667999267578,
+        0.2772078216075897
+      ],
+      [
+        0.191814124584198,
+        0.007811395917087793,
+        0.20961053669452667,
+        176.0531463623047,
+        -0.003147333627566695,
+        90.02890014648438,
+        0.409576952457428
+      ],
+      [
+        0.19489523768424988,
+        0.008414356037974358,
+        0.20946261286735535,
+        176.0500946044922,
+        -0.0037130373530089855,
+        90.02975463867188,
+        0.32769688963890076
+      ]
+    ],
+    [
+      [
+        0.15320706367492676,
+        0.003877518232911825,
+        0.2095641791820526,
+        176.0668487548828,
+        -0.001166658359579742,
+        90.01170349121094,
+        0.6711252927780151
+      ],
+      [
+        0.1566144973039627,
+        0.004569030366837978,
+        0.20953533053398132,
+        176.06362915039062,
+        -0.0020470903255045414,
+        90.01277160644531,
+        0.843066930770874
+      ],
+      [
+        0.16024918854236603,
+        0.00502287270501256,
+        0.20954887568950653,
+        176.0618896484375,
+        -0.0027252447325736284,
+        90.01306915283203,
+        0.8675051331520081
+      ],
+      [
+        0.16360752284526825,
+        0.0056188576854765415,
+        0.20952168107032776,
+        176.06117248535156,
+        -0.0028743562288582325,
+        90.01525115966797,
+        0.7952479720115662
+      ],
+      [
+        0.16767333447933197,
+        0.006405533291399479,
+        0.20970648527145386,
+        176.0563507080078,
+        -0.002970325294882059,
+        90.01740264892578,
+        0.7962521910667419
+      ],
+      [
+        0.1707918345928192,
+        0.0068721105344593525,
+        0.2097848653793335,
+        176.05723571777344,
+        -0.00312036438845098,
+        90.0158462524414,
+        0.757518470287323
+      ],
+      [
+        0.1740151196718216,
+        0.0074592093005776405,
+        0.20961347222328186,
+        176.0572967529297,
+        -0.0039813644252717495,
+        90.01248931884766,
+        0.8143046498298645
+      ],
+      [
+        0.17717716097831726,
+        0.007950231432914734,
+        0.20968925952911377,
+        176.0526123046875,
+        -0.00506437337026,
+        90.01351165771484,
+        0.8191958665847778
+      ],
+      [
+        0.1810409277677536,
+        0.008750645443797112,
+        0.2097550928592682,
+        176.05140686035156,
+        -0.006298130378127098,
+        90.01214599609375,
+        0.8906230926513672
+      ],
+      [
+        0.1847178339958191,
+        0.009585577063262463,
+        0.20978476107120514,
+        176.05023193359375,
+        -0.007116713095456362,
+        90.01170349121094,
+        0.8436222076416016
+      ],
+      [
+        0.18780097365379333,
+        0.010180408135056496,
+        0.20972739160060883,
+        176.04714965820312,
+        -0.007670117076486349,
+        90.01290893554688,
+        0.6384347081184387
+      ],
+      [
+        0.19051514565944672,
+        0.010563481599092484,
+        0.20976945757865906,
+        176.04820251464844,
+        -0.006965750828385353,
+        90.01403045654297,
+        0.7892048358917236
+      ],
+      [
+        0.19445815682411194,
+        0.011537492275238037,
+        0.21000604331493378,
+        176.04360961914062,
+        -0.008108519949018955,
+        90.0125732421875,
+        0.7036859393119812
+      ],
+      [
+        0.19760701060295105,
+        0.012063547968864441,
+        0.20986729860305786,
+        176.0408477783203,
+        -0.008672082796692848,
+        90.01353454589844,
+        0.8137848377227783
+      ],
+      [
+        0.20081570744514465,
+        0.012924863956868649,
+        0.2099616974592209,
+        176.03785705566406,
+        -0.008717702701687813,
+        90.0132827758789,
+        0.7565792202949524
+      ],
+      [
+        0.20393992960453033,
+        0.013328734785318375,
+        0.21003961563110352,
+        176.03724670410156,
+        -0.008869550190865993,
+        90.0144271850586,
+        0.5986968278884888
+      ]
+    ],
+    [
+      [
+        0.15570738911628723,
+        0.004031960386782885,
+        0.20950137078762054,
+        176.06748962402344,
+        0.0008320023771375418,
+        90.02385711669922,
+        0.26037225127220154
+      ],
+      [
+        0.1596946120262146,
+        0.0049184272065758705,
+        0.20922081172466278,
+        176.0600128173828,
+        -0.0004946738481521606,
+        90.03562927246094,
+        0.3610609173774719
+      ],
+      [
+        0.16316814720630646,
+        0.0051771411672234535,
+        0.20932269096374512,
+        176.0504913330078,
+        0.00031913636485114694,
+        90.04635620117188,
+        0.4131803512573242
+      ],
+      [
+        0.16617445647716522,
+        0.005922356154769659,
+        0.20914535224437714,
+        176.04766845703125,
+        0.0007085510296747088,
+        90.05162048339844,
+        0.25813931226730347
+      ],
+      [
+        0.16922764480113983,
+        0.00679750507697463,
+        0.2091502845287323,
+        176.04852294921875,
+        -0.0003173237491864711,
+        90.05496215820312,
+        0.33611470460891724
+      ],
+      [
+        0.17232975363731384,
+        0.007941234856843948,
+        0.20914505422115326,
+        176.04612731933594,
+        -0.0014954834477975965,
+        90.05684661865234,
+        0.3006240725517273
+      ],
+      [
+        0.17566494643688202,
+        0.00892396830022335,
+        0.20918969810009003,
+        176.04808044433594,
+        -0.00037949191755615175,
+        90.06536102294922,
+        0.30304014682769775
+      ],
+      [
+        0.1784704029560089,
+        0.009752323850989342,
+        0.20918208360671997,
+        176.0474090576172,
+        -0.0005751223070546985,
+        90.07138061523438,
+        0.35009118914604187
+      ],
+      [
+        0.1819218546152115,
+        0.010791818611323833,
+        0.2091527283191681,
+        176.04249572753906,
+        -0.0007570591405965388,
+        90.07756805419922,
+        0.2684592306613922
+      ],
+      [
+        0.18544505536556244,
+        0.012088554911315441,
+        0.20915399491786957,
+        176.04083251953125,
+        -0.002598742488771677,
+        90.07618713378906,
+        0.48995068669319153
+      ],
+      [
+        0.18818335235118866,
+        0.013036024756729603,
+        0.20932374894618988,
+        176.0377655029297,
+        -0.0022734468802809715,
+        90.08090209960938,
+        0.2815437316894531
+      ],
+      [
+        0.19041289389133453,
+        0.014055602252483368,
+        0.20929141342639923,
+        176.04513549804688,
+        -0.001175259705632925,
+        90.08084869384766,
+        0.304794579744339
+      ],
+      [
+        0.19379822909832,
+        0.015518001280725002,
+        0.2092347890138626,
+        176.03819274902344,
+        -0.00430992292240262,
+        90.0817642211914,
+        0.22246555984020233
+      ],
+      [
+        0.19677460193634033,
+        0.016914932057261467,
+        0.2091177999973297,
+        176.03799438476562,
+        -0.0033689693082123995,
+        90.0866928100586,
+        0.15583205223083496
+      ],
+      [
+        0.19934877753257751,
+        0.018543174490332603,
+        0.20896847546100616,
+        176.03729248046875,
+        -0.003937090281397104,
+        90.08851623535156,
+        0.1489855796098709
+      ],
+      [
+        0.20130197703838348,
+        0.019675541669130325,
+        0.2090020775794983,
+        176.0382843017578,
+        -0.004012223798781633,
+        90.09304809570312,
+        0.25474265217781067
+      ]
+    ],
+    [
+      [
+        0.15965940058231354,
+        0.0043397825211286545,
+        0.209621861577034,
+        176.0689239501953,
+        -0.0011879531666636467,
+        90.02320098876953,
+        0.6312397122383118
+      ],
+      [
+        0.16217365860939026,
+        0.0043952446430921555,
+        0.20965008437633514,
+        176.06741333007812,
+        -0.0007959406357258558,
+        90.02256774902344,
+        0.560364842414856
+      ],
+      [
+        0.1667148470878601,
+        0.004959119483828545,
+        0.2096257358789444,
+        176.06631469726562,
+        -0.0010053787846118212,
+        90.02458953857422,
+        0.6710071563720703
+      ],
+      [
+        0.16926433145999908,
+        0.00525676179677248,
+        0.2097669392824173,
+        176.0628204345703,
+        -0.002619990846142173,
+        90.02403259277344,
+        0.8193199038505554
+      ],
+      [
+        0.17260019481182098,
+        0.005627262871712446,
+        0.2096869796514511,
+        176.06051635742188,
+        -0.0023580575361847878,
+        90.02559661865234,
+        0.6407030820846558
+      ],
+      [
+        0.17537415027618408,
+        0.005928244441747665,
+        0.20979009568691254,
+        176.06105041503906,
+        -0.0029288141522556543,
+        90.02571105957031,
+        0.5887490510940552
+      ],
+      [
+        0.1782412827014923,
+        0.006451264023780823,
+        0.2097741812467575,
+        176.06076049804688,
+        -0.0031277562957257032,
+        90.02436065673828,
+        0.5876089334487915
+      ],
+      [
+        0.18165722489356995,
+        0.006829468533396721,
+        0.20969098806381226,
+        176.0586395263672,
+        -0.00429123267531395,
+        90.02703857421875,
+        0.5860849022865295
+      ],
+      [
+        0.185263529419899,
+        0.007085599936544895,
+        0.20965443551540375,
+        176.05691528320312,
+        -0.0043725185096263885,
+        90.02728271484375,
+        0.5322582125663757
+      ],
+      [
+        0.18779006600379944,
+        0.007498227991163731,
+        0.2097581923007965,
+        176.0555419921875,
+        -0.0049880146980285645,
+        90.02754211425781,
+        0.6229104995727539
+      ],
+      [
+        0.19105805456638336,
+        0.007973063737154007,
+        0.20969772338867188,
+        176.0548553466797,
+        -0.005742683075368404,
+        90.0288314819336,
+        0.5579782724380493
+      ],
+      [
+        0.19413703680038452,
+        0.008566942065954208,
+        0.2096378654241562,
+        176.05535888671875,
+        -0.005284748040139675,
+        90.03034973144531,
+        0.5289738178253174
+      ],
+      [
+        0.197028249502182,
+        0.009145704098045826,
+        0.20970022678375244,
+        176.05313110351562,
+        -0.0053259809501469135,
+        90.03465270996094,
+        0.5114293694496155
+      ],
+      [
+        0.20025233924388885,
+        0.009961273521184921,
+        0.20975342392921448,
+        176.0496826171875,
+        -0.005832890048623085,
+        90.0359878540039,
+        0.453940212726593
+      ],
+      [
+        0.20342357456684113,
+        0.010857763700187206,
+        0.20972304046154022,
+        176.04771423339844,
+        -0.005916388239711523,
+        90.03768157958984,
+        0.4093288481235504
+      ],
+      [
+        0.20566204190254211,
+        0.011463065631687641,
+        0.20958004891872406,
+        176.0487823486328,
+        -0.006741437129676342,
+        90.04026794433594,
+        0.462369829416275
+      ]
+    ],
+    [
+      [
+        0.1627015769481659,
+        0.004767170641571283,
+        0.20974454283714294,
+        176.0701446533203,
+        -0.002045096829533577,
+        90.0233154296875,
+        0.7582746148109436
+      ],
+      [
+        0.16562864184379578,
+        0.0051562669686973095,
+        0.20952576398849487,
+        176.06871032714844,
+        -0.0027279285714030266,
+        90.0236587524414,
+        0.8048058152198792
+      ],
+      [
+        0.1691281944513321,
+        0.00562769640237093,
+        0.20948272943496704,
+        176.0654296875,
+        -0.0032336099538952112,
+        90.02269744873047,
+        0.8207907676696777
+      ],
+      [
+        0.171921968460083,
+        0.006076496094465256,
+        0.2094859629869461,
+        176.06527709960938,
+        -0.003252998925745487,
+        90.02222442626953,
+        0.8254575133323669
+      ],
+      [
+        0.17476625740528107,
+        0.006483426317572594,
+        0.20935216546058655,
+        176.0651092529297,
+        -0.004123971797525883,
+        90.01970672607422,
+        0.7532180547714233
+      ],
+      [
+        0.17783357203006744,
+        0.006940271705389023,
+        0.20938663184642792,
+        176.0624542236328,
+        -0.0038694136310368776,
+        90.02134704589844,
+        0.7255072593688965
+      ],
+      [
+        0.18114420771598816,
+        0.007429271470755339,
+        0.20929883420467377,
+        176.0595703125,
+        -0.003606880782172084,
+        90.02422332763672,
+        0.7561952471733093
+      ],
+      [
+        0.18437445163726807,
+        0.008040769957005978,
+        0.20931850373744965,
+        176.05868530273438,
+        -0.00382827571593225,
+        90.02410888671875,
+        0.6625480055809021
+      ],
+      [
+        0.18748801946640015,
+        0.00863991491496563,
+        0.20934908092021942,
+        176.05992126464844,
+        -0.004143931902945042,
+        90.02449035644531,
+        0.7723160982131958
+      ],
+      [
+        0.19126126170158386,
+        0.009501046501100063,
+        0.20913909375667572,
+        176.0572052001953,
+        -0.004465906415134668,
+        90.02906799316406,
+        0.4436911940574646
+      ],
+      [
+        0.19416716694831848,
+        0.010205825790762901,
+        0.20915868878364563,
+        176.05453491210938,
+        -0.004463860299438238,
+        90.027587890625,
+        0.5903676152229309
+      ],
+      [
+        0.19717185199260712,
+        0.010860432870686054,
+        0.2091618925333023,
+        176.05538940429688,
+        -0.005199267994612455,
+        90.02757263183594,
+        0.6078943610191345
+      ],
+      [
+        0.20031048357486725,
+        0.011642749421298504,
+        0.20912106335163116,
+        176.05511474609375,
+        -0.005928014405071735,
+        90.02796173095703,
+        0.6361249685287476
+      ],
+      [
+        0.20392723381519318,
+        0.01258503645658493,
+        0.20941230654716492,
+        176.0525360107422,
+        -0.006043020635843277,
+        90.02578735351562,
+        0.5885009169578552
+      ],
+      [
+        0.2072317898273468,
+        0.013506201095879078,
+        0.20940367877483368,
+        176.049560546875,
+        -0.006071696989238262,
+        90.02678680419922,
+        0.5975212454795837
+      ],
+      [
+        0.2100575864315033,
+        0.014562325552105904,
+        0.2093134969472885,
+        176.0478973388672,
+        -0.0073579587042331696,
+        90.0262680053711,
+        0.6309738755226135
+      ]
+    ],
+    [
+      [
+        0.1651243418455124,
+        0.004650649148970842,
+        0.2099265307188034,
+        176.0723419189453,
+        -0.0020860014483332634,
+        90.02278900146484,
+        0.6095306873321533
+      ],
+      [
+        0.16753995418548584,
+        0.004822032991796732,
+        0.20993070304393768,
+        176.07127380371094,
+        -0.0024738460779190063,
+        90.02287292480469,
+        0.674486517906189
+      ],
+      [
+        0.1708865463733673,
+        0.005058744922280312,
+        0.2098793387413025,
+        176.068359375,
+        -0.0024691452272236347,
+        90.0244140625,
+        0.5181872248649597
+      ],
+      [
+        0.1745997816324234,
+        0.0053174421191215515,
+        0.20972159504890442,
+        176.0693817138672,
+        -0.0028478067833930254,
+        90.02423858642578,
+        0.7765102386474609
+      ],
+      [
+        0.17773738503456116,
+        0.005469720344990492,
+        0.20982065796852112,
+        176.06507873535156,
+        -0.0025920160114765167,
+        90.02468872070312,
+        0.6843870282173157
+      ],
+      [
+        0.18066078424453735,
+        0.00577480299398303,
+        0.20992393791675568,
+        176.06198120117188,
+        -0.003113750834017992,
+        90.02423095703125,
+        0.7013289332389832
+      ],
+      [
+        0.1831689178943634,
+        0.006065744441002607,
+        0.20992209017276764,
+        176.05795288085938,
+        -0.0030602707993239164,
+        90.02643585205078,
+        0.518021821975708
+      ],
+      [
+        0.18636475503444672,
+        0.006411133334040642,
+        0.20998111367225647,
+        176.05857849121094,
+        -0.003373814281076193,
+        90.02595520019531,
+        0.379337877035141
+      ],
+      [
+        0.1895712912082672,
+        0.006721135228872299,
+        0.20990917086601257,
+        176.0587158203125,
+        -0.0028866035863757133,
+        90.02997589111328,
+        0.4883025586605072
+      ],
+      [
+        0.19290366768836975,
+        0.007123997900635004,
+        0.21008679270744324,
+        176.05540466308594,
+        -0.0034077009186148643,
+        90.02896118164062,
+        0.5834147930145264
+      ],
+      [
+        0.19599564373493195,
+        0.007611199282109737,
+        0.210096538066864,
+        176.0533447265625,
+        -0.003941349685192108,
+        90.03162384033203,
+        0.4957633912563324
+      ],
+      [
+        0.19921129941940308,
+        0.0080696577206254,
+        0.2101949006319046,
+        176.04644775390625,
+        -0.00373558490537107,
+        90.03898620605469,
+        0.3913472592830658
+      ],
+      [
+        0.20198361575603485,
+        0.008670545183122158,
+        0.2100811004638672,
+        176.04823303222656,
+        -0.004277922213077545,
+        90.03763580322266,
+        0.5152808427810669
+      ],
+      [
+        0.2047715038061142,
+        0.00912513304501772,
+        0.21017470955848694,
+        176.04974365234375,
+        -0.0037934803403913975,
+        90.03868865966797,
+        0.48113709688186646
+      ],
+      [
+        0.20853449404239655,
+        0.009845931082963943,
+        0.21011631190776825,
+        176.0499725341797,
+        -0.003713260404765606,
+        90.04168701171875,
+        0.383797824382782
+      ],
+      [
+        0.2110190987586975,
+        0.010597487911581993,
+        0.2101048082113266,
+        176.04354858398438,
+        -0.004673217423260212,
+        90.04524230957031,
+        0.321653813123703
+      ]
+    ]
+  ]
+}

bundle/artifacts/sim/session_20260327_165944_bear_scene_fit.json

@@ -0,0 +1,123 @@
+{
+  "session_root": "/workspace/data/teddybear_raw/session_20260327_165944_bear",
+  "sync_row_index": 0,
+  "azure_rgb_seq": 10,
+  "azure_depth_seq": 10,
+  "robot_seq": 37,
+  "rgb_path": "/workspace/data/teddybear_raw/session_20260327_165944_bear/azure_rgb/000010.jpg",
+  "depth_path": "/workspace/data/teddybear_raw/session_20260327_165944_bear/azure_depth/000010.png",
+  "table_corners_px": [
+    [
+      192.0,
+      138.0
+    ],
+    [
+      1635.0,
+      46.0
+    ],
+    [
+      1712.0,
+      787.0
+    ],
+    [
+      185.0,
+      858.0
+    ]
+  ],
+  "table_size_m": [
+    1.52,
+    0.76
+  ],
+  "camera_focal_px": 650.0,
+  "rvec": [
+    -0.09174914809320757,
+    0.01916933742850816,
+    -0.052062912426137815
+  ],
+  "tvec": [
+    -0.045156570030564204,
+    -0.10354127538092003,
+    0.6651521249148916
+  ],
+  "robot_base_world": [
+    0.5,
+    0.45,
+    -0.45
+  ],
+  "robot_base_yaw_rad": -2.0,
+  "sync_row": {
+    "t_sync_sec": 2.750489500002004,
+    "t_sync_wall_time_sec": 1774645185.553682,
+    "rgb_seq": 0,
+    "rgb_t_sec": 2.973579300014535,
+    "rgb_file": "rgb/000000.jpg",
+    "rgb_age_sec": -0.2230898000125307,
+    "depth_seq": 17,
+    "depth_t_sec": 2.750489500002004,
+    "depth_file": "depth/000017.png",
+    "depth_age_sec": 0.0,
+    "azure_rgb_seq": 10,
+    "azure_rgb_t_sec": 2.7677244000078645,
+    "azure_rgb_file": "azure_rgb/000010.jpg",
+    "azure_rgb_age_sec": -0.0172349000058602,
+    "azure_depth_seq": 10,
+    "azure_depth_t_sec": 2.7677244000078645,
+    "azure_depth_file": "azure_depth/000010.png",
+    "azure_depth_age_sec": -0.0172349000058602,
+    "robot_seq": 37,
+    "robot_t_sec": 2.7384919000032824,
+    "robot_age_sec": 0.0119975999987218,
+    "tool_x_m": 0.1304228156805038,
+    "tool_y_m": 0.0013459887122735,
+    "tool_z_m": 0.2086409330368042,
+    "tool_theta_x_deg": 176.00607299804688,
+    "tool_theta_y_deg": -0.0013248149771243,
+    "tool_theta_z_deg": 89.99845886230469,
+    "cam_x_m": NaN,
+    "cam_y_m": NaN,
+    "cam_z_m": NaN,
+    "q1_deg": 0.0008167303167283,
+    "q2_deg": 340.0004272460937,
+    "q3_deg": 180.00140380859375,
+    "q4_deg": 213.99542236328125,
+    "q5_deg": 359.9988708496094,
+    "q6_deg": 309.9989318847656,
+    "q7_deg": 90.00142669677734,
+    "gripper_pos": 4.80349588394165
+  },
+  "table_depth_mm": 975.0,
+  "box_height_m": 0.076,
+  "teddy_height_m": 0.099,
+  "box_world_polygon": [
+    [
+      0.22150550362135488,
+      0.24670163116856353,
+      0.0
+    ],
+    [
+      0.22071163420598972,
+      -0.09764576770064368,
+      0.0
+    ],
+    [
+      0.4771996794142288,
+      -0.10335451470612148,
+      0.0
+    ],
+    [
+      0.46592016136934344,
+      0.23941644811537086,
+      0.0
+    ]
+  ],
+  "teddy_world_center": [
+    0.17440522310559595,
+    0.10013784762023073,
+    0.0
+  ],
+  "teddy_world_extent": [
+    0.24986488552884822,
+    0.2295185354746066,
+    0.0
+  ]
+}

bundle/code/openpi/pyproject.toml

@@ -0,0 +1,137 @@
+[project]
+name = "openpi"
+version = "0.1.0"
+description = "Physical Intelligence open source repo"
+readme = "README.md"
+requires-python = ">=3.11"
+license = { file = "LICENSE" }
+dependencies = [
+    "augmax>=0.3.4",
+    "dm-tree>=0.1.8",
+    "einops>=0.8.0",
+    "equinox>=0.11.8",
+    "flatbuffers>=24.3.25",
+    "flax==0.10.2",
+    "fsspec[gcs]>=2024.6.0",
+    "gym-aloha>=0.1.1",
+    "imageio>=2.36.1",
+    "jax[cuda12]==0.5.3",
+    "jaxtyping==0.2.36",
+    "lerobot",
+    "ml_collections==1.0.0",
+    "numpy>=1.22.4,<2.0.0",
+    "numpydantic>=1.6.6",
+    "opencv-python>=4.10.0.84",
+    "openpi-client",
+    "orbax-checkpoint==0.11.13",
+    "pillow>=11.0.0",
+    "sentencepiece>=0.2.0",
+    "torch==2.7.1",
+    "tqdm-loggable>=0.2",
+    "typing-extensions>=4.12.2",
+    "tyro>=0.9.5",
+    "wandb>=0.19.1",
+    "filelock>=3.16.1",
+    "beartype==0.19.0",
+    "treescope>=0.1.7",
+    "transformers==4.53.2",
+    "rich>=14.0.0",
+    "polars>=1.30.0",
+]
+
+
+[project.urls]
+Repository = "https://github.com/Physical-Intelligence/openpi"
+
+[dependency-groups]
+dev = [
+    "pytest>=8.3.4",
+    "ruff>=0.8.6",
+    "pre-commit>=4.0.1",
+    "ipykernel>=6.29.5",
+    "ipywidgets>=8.1.5",
+    "matplotlib>=3.10.0",
+    "pynvml>=12.0.0",
+]
+rlds = [
+    "dlimp",
+    "tensorflow-cpu==2.15.0",
+    "tensorflow-datasets==4.9.9",
+]
+
+[tool.uv]
+override-dependencies = ["ml-dtypes==0.4.1", "tensorstore==0.1.74"]
+
+[tool.uv.sources]
+openpi-client = { workspace = true }
+lerobot = { git = "https://github.com/huggingface/lerobot", rev = "0cf864870cf29f4738d3ade893e6fd13fbd7cdb5" }
+dlimp = { git = "https://github.com/kvablack/dlimp", rev = "ad72ce3a9b414db2185bc0b38461d4101a65477a" }
+
+[tool.uv.workspace]
+members = ["packages/*"]
+
+[tool.ruff]
+line-length = 120
+target-version = "py311"
+extend-exclude = ["docker", "third_party", "src/openpi/models_pytorch/transformers_replace/*"]
+
+[tool.ruff.lint]
+# https://docs.astral.sh/ruff/rules/
+select = [
+    "B",
+    "C4",
+    "DTZ",
+    "E4",
+    "E7",
+    "E9",
+    "F",
+    "FBT",
+    "FURB",
+    "I",
+    "ICN",
+    "ISC",
+    "LOG",
+    "N",
+    "PD",
+    "PERF",
+    "PIE",
+    "PLC",
+    "PLE",
+    "PLR1",
+    "PLR5",
+    "PLW",
+    "PT",
+    "Q",
+    "RET",
+    "RUF",
+    "SIM",
+    "SLF",
+    "T10",
+    "T20",
+    "UP",
+    "W",
+]
+ignore = [
+    "F722",   # Conflicts with array typing.
+    "T201",   # We use print statements.
+    "PD008",  # Lots of false positives.
+    "ISC001", # Disabling to support ruff format.
+    "LOG015", # Use logger.info.
+]
+unfixable = [
+    "B905", # Fix defaults to strict=False, which is not what we want.
+]
+
+[tool.ruff.lint.isort]
+force-single-line = true
+force-sort-within-sections = true
+single-line-exclusions = ["collections.abc", "typing", "typing_extensions"]
+known-third-party = ["wandb"]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.pytest.ini_options]
+markers = ["manual: should be run manually."]
+testpaths = ["src", "scripts", "packages"]

bundle/code/openpi/scripts/archive_teddybear_checkpoints_to_hf.sh

@@ -0,0 +1,89 @@
+#!/usr/bin/env bash
+set -uo pipefail
+
+if [[ $# -lt 4 ]]; then
+  echo "usage: $0 <repo_id> <checkpoint_root> <status_log> <state_file> [interval_s]" >&2
+  exit 1
+fi
+
+REPO_ID="$1"
+CHECKPOINT_ROOT="$2"
+STATUS_LOG="$3"
+STATE_FILE="$4"
+INTERVAL_S="${5:-120}"
+
+mkdir -p "$(dirname "$STATUS_LOG")"
+mkdir -p "$(dirname "$STATE_FILE")"
+touch "$STATE_FILE"
+
+log() {
+  printf '%s | %s\n' "$(date -u +%FT%TZ)" "$*" >> "$STATUS_LOG"
+}
+
+already_done() {
+  grep -qx "$1" "$STATE_FILE" 2>/dev/null
+}
+
+mark_done() {
+  printf '%s\n' "$1" >> "$STATE_FILE"
+}
+
+checkpoint_ready() {
+  local step="$1"
+  local src="${CHECKPOINT_ROOT}/${step}"
+  [[ -f "${src}/_CHECKPOINT_METADATA" && -f "${src}/params/_METADATA" ]]
+}
+
+verify_remote_step() {
+  local step="$1"
+  local verify_dir="/workspace/hf_verify_${step}"
+  rm -rf "$verify_dir"
+  mkdir -p "$verify_dir"
+  hf download "$REPO_ID" "${step}/_CHECKPOINT_METADATA" --type model --local-dir "$verify_dir" >/dev/null
+  hf download "$REPO_ID" "${step}/params/_METADATA" --type model --local-dir "$verify_dir" >/dev/null
+}
+
+archive_step() {
+  local step="$1"
+  local src="${CHECKPOINT_ROOT}/${step}"
+  local stage="/workspace/hf_stage_${step}"
+
+  if [[ ! -d "$src" ]]; then
+    log "skip step=${step} missing_local_dir"
+    return 0
+  fi
+
+  rm -rf "$stage"
+  mkdir -p "$stage"
+  cp -al "$src" "$stage/"
+  log "upload_start step=${step}"
+  hf upload-large-folder "$REPO_ID" "$stage" --type model --num-workers 8 --no-bars >> "$STATUS_LOG" 2>&1
+  verify_remote_step "$step"
+  rm -rf "$src" "$stage"
+  mark_done "$step"
+  log "upload_done step=${step}"
+}
+
+log "archiver_start repo=${REPO_ID} root=${CHECKPOINT_ROOT}"
+
+while true; do
+  if [[ ! -d "$CHECKPOINT_ROOT" ]]; then
+    log "checkpoint_root_missing"
+    sleep "$INTERVAL_S"
+    continue
+  fi
+
+  while IFS= read -r step; do
+    [[ -z "$step" ]] && continue
+    if already_done "$step"; then
+      continue
+    fi
+    if ! checkpoint_ready "$step"; then
+      log "skip step=${step} checkpoint_not_ready"
+      continue
+    fi
+    archive_step "$step" || log "upload_failed step=${step}"
+  done < <(find "$CHECKPOINT_ROOT" -maxdepth 1 -mindepth 1 -type d -printf '%f\n' | rg '^[0-9]+$' | sort -n)
+
+  sleep "$INTERVAL_S"
+done

bundle/code/openpi/scripts/bench_first_kinova_batch.py

@@ -0,0 +1,28 @@
+import dataclasses
+import os
+import time
+
+from openpi.training import config as c
+from openpi.training import data_loader as dl
+
+
+def main() -> None:
+    workers = int(os.environ.get("KINOVA_BENCH_WORKERS", "12"))
+    batch_size = int(os.environ.get("KINOVA_BENCH_BATCH_SIZE", "24"))
+    cfg = dataclasses.replace(
+        c.get_config("pi05_kinova_teddybear"),
+        exp_name=f"bench_first_b{batch_size}_w{workers}",
+        batch_size=batch_size,
+        num_workers=workers,
+    )
+    print({"workers": workers, "batch_size": batch_size}, flush=True)
+    t0 = time.time()
+    loader = dl.create_data_loader(cfg, shuffle=False, num_batches=1)
+    print({"loader_created_s": round(time.time() - t0, 3)}, flush=True)
+    t1 = time.time()
+    _ = next(iter(loader))
+    print({"first_batch_s": round(time.time() - t1, 3)}, flush=True)
+
+
+if __name__ == "__main__":
+    main()

bundle/code/openpi/scripts/bench_kinova_loader.py

@@ -0,0 +1,39 @@
+import dataclasses
+import time
+
+from openpi.training import config as c
+from openpi.training import data_loader as dl
+
+
+def main() -> None:
+    for workers in [16, 32, 48]:
+        cfg = dataclasses.replace(
+            c.get_config("pi05_kinova_teddybear"),
+            exp_name=f"bench_{workers}",
+            num_workers=workers,
+        )
+        t0 = time.time()
+        loader = dl.create_data_loader(cfg, shuffle=True, num_batches=6, skip_norm_stats=True)
+        loader_s = time.time() - t0
+
+        it = iter(loader)
+        t1 = time.time()
+        first_batch_s = None
+        for i in range(6):
+            _ = next(it)
+            if i == 0:
+                first_batch_s = time.time() - t1
+        total_s = time.time() - t1
+        print(
+            {
+                "workers": workers,
+                "loader_s": round(loader_s, 3),
+                "first_batch_s": round(first_batch_s or 0.0, 3),
+                "avg_batch_s": round(total_s / 6, 3),
+            },
+            flush=True,
+        )
+
+
+if __name__ == "__main__":
+    main()

bundle/code/openpi/scripts/compute_norm_stats_kinova_teddybear.py

@@ -0,0 +1,58 @@
+"""Compute norm stats for the Kinova TeddyBear dataset without decoding videos.
+
+This matches the training-time state/action preprocessing for the custom
+`pi05_kinova_teddybear` config:
+- state stats are computed on raw `observation.state`
+- action stats are computed on 16-step action chunks after converting the
+  first 6 dimensions from absolute targets to deltas relative to the current state
+  while leaving the gripper dimension absolute
+"""
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from openpi.shared import normalize
+from openpi.training import config as config_lib
+from openpi.training.kinova_lerobot_v3_dataset import locate_dataset_root
+
+
+def main() -> None:
+    cfg = config_lib.get_config("pi05_kinova_teddybear")
+    repo_id = "lsnu/TeddyBearKinovaTestSetLeRobot"
+    root = locate_dataset_root()
+
+    data_df = pd.concat(
+        [pd.read_parquet(path) for path in sorted((root / "data").rglob("*.parquet"))],
+        ignore_index=True,
+    ).sort_values("index")
+
+    state_stats = normalize.RunningStats()
+    action_stats = normalize.RunningStats()
+    horizon = cfg.model.action_horizon
+
+    for _, ep_rows in data_df.groupby("episode_index", sort=True):
+        states = np.stack(ep_rows["observation.state"].to_list()).astype(np.float32)
+        actions = np.stack(ep_rows["action"].to_list()).astype(np.float32)
+        length = len(states)
+
+        state_stats.update(states)
+
+        idx = np.arange(length)[:, None] + np.arange(horizon)[None, :]
+        idx = np.clip(idx, 0, length - 1)
+        action_chunks = actions[idx]
+        action_chunks[..., :6] -= states[:, None, :6]
+        action_stats.update(action_chunks)
+
+    output_path = cfg.assets_dirs / repo_id
+    norm_stats = {
+        "state": state_stats.get_statistics(),
+        "actions": action_stats.get_statistics(),
+    }
+    print(f"Writing stats to: {output_path}")
+    normalize.save(output_path, norm_stats)
+
+
+if __name__ == "__main__":
+    main()

bundle/code/openpi/scripts/serve_policy.py

@@ -0,0 +1,122 @@
+import dataclasses
+import enum
+import logging
+import socket
+
+import tyro
+
+from openpi.policies import policy as _policy
+from openpi.policies import policy_config as _policy_config
+from openpi.serving import websocket_policy_server
+from openpi.training import config as _config
+
+
+class EnvMode(enum.Enum):
+    """Supported environments."""
+
+    ALOHA = "aloha"
+    ALOHA_SIM = "aloha_sim"
+    DROID = "droid"
+    LIBERO = "libero"
+
+
+@dataclasses.dataclass
+class Checkpoint:
+    """Load a policy from a trained checkpoint."""
+
+    # Training config name (e.g., "pi0_aloha_sim").
+    config: str
+    # Checkpoint directory (e.g., "checkpoints/pi0_aloha_sim/exp/10000").
+    dir: str
+
+
+@dataclasses.dataclass
+class Default:
+    """Use the default policy for the given environment."""
+
+
+@dataclasses.dataclass
+class Args:
+    """Arguments for the serve_policy script."""
+
+    # Environment to serve the policy for. This is only used when serving default policies.
+    env: EnvMode = EnvMode.ALOHA_SIM
+
+    # If provided, will be used in case the "prompt" key is not present in the data, or if the model doesn't have a default
+    # prompt.
+    default_prompt: str | None = None
+
+    # Port to serve the policy on.
+    port: int = 8000
+    # Record the policy's behavior for debugging.
+    record: bool = False
+
+    # Specifies how to load the policy. If not provided, the default policy for the environment will be used.
+    policy: Checkpoint | Default = dataclasses.field(default_factory=Default)
+
+
+# Default checkpoints that should be used for each environment.
+DEFAULT_CHECKPOINT: dict[EnvMode, Checkpoint] = {
+    EnvMode.ALOHA: Checkpoint(
+        config="pi05_aloha",
+        dir="gs://openpi-assets/checkpoints/pi05_base",
+    ),
+    EnvMode.ALOHA_SIM: Checkpoint(
+        config="pi0_aloha_sim",
+        dir="gs://openpi-assets/checkpoints/pi0_aloha_sim",
+    ),
+    EnvMode.DROID: Checkpoint(
+        config="pi05_droid",
+        dir="gs://openpi-assets/checkpoints/pi05_droid",
+    ),
+    EnvMode.LIBERO: Checkpoint(
+        config="pi05_libero",
+        dir="gs://openpi-assets/checkpoints/pi05_libero",
+    ),
+}
+
+
+def create_default_policy(env: EnvMode, *, default_prompt: str | None = None) -> _policy.Policy:
+    """Create a default policy for the given environment."""
+    if checkpoint := DEFAULT_CHECKPOINT.get(env):
+        return _policy_config.create_trained_policy(
+            _config.get_config(checkpoint.config), checkpoint.dir, default_prompt=default_prompt
+        )
+    raise ValueError(f"Unsupported environment mode: {env}")
+
+
+def create_policy(args: Args) -> _policy.Policy:
+    """Create a policy from the given arguments."""
+    match args.policy:
+        case Checkpoint():
+            return _policy_config.create_trained_policy(
+                _config.get_config(args.policy.config), args.policy.dir, default_prompt=args.default_prompt
+            )
+        case Default():
+            return create_default_policy(args.env, default_prompt=args.default_prompt)
+
+
+def main(args: Args) -> None:
+    policy = create_policy(args)
+    policy_metadata = policy.metadata
+
+    # Record the policy's behavior.
+    if args.record:
+        policy = _policy.PolicyRecorder(policy, "policy_records")
+
+    hostname = socket.gethostname()
+    local_ip = socket.gethostbyname(hostname)
+    logging.info("Creating server (host: %s, ip: %s)", hostname, local_ip)
+
+    server = websocket_policy_server.WebsocketPolicyServer(
+        policy=policy,
+        host="0.0.0.0",
+        port=args.port,
+        metadata=policy_metadata,
+    )
+    server.serve_forever()
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, force=True)
+    main(tyro.cli(Args))

bundle/code/openpi/scripts/stop_train_at_checkpoint.sh

@@ -0,0 +1,44 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+if [[ $# -lt 3 ]]; then
+  echo "usage: $0 <pid> <checkpoint_root> <target_step> [status_log] [interval_s]" >&2
+  exit 1
+fi
+
+PID="$1"
+CHECKPOINT_ROOT="$2"
+TARGET_STEP="$3"
+STATUS_LOG="${4:-/workspace/openpi/stop_at_checkpoint.log}"
+INTERVAL_S="${5:-15}"
+
+mkdir -p "$(dirname "$STATUS_LOG")"
+
+log() {
+  printf '%s | %s\n' "$(date -u +%FT%TZ)" "$*" >> "$STATUS_LOG"
+}
+
+checkpoint_ready() {
+  local step="$1"
+  local src="${CHECKPOINT_ROOT}/${step}"
+  [[ -f "${src}/_CHECKPOINT_METADATA" && -f "${src}/params/_METADATA" ]]
+}
+
+log "watch_start pid=${PID} target_step=${TARGET_STEP} checkpoint_root=${CHECKPOINT_ROOT}"
+
+while kill -0 "$PID" 2>/dev/null; do
+  if checkpoint_ready "$TARGET_STEP"; then
+    log "checkpoint_ready step=${TARGET_STEP}"
+    kill -TERM "$PID"
+    log "signal_sent signal=TERM pid=${PID}"
+    sleep 5
+    if kill -0 "$PID" 2>/dev/null; then
+      kill -INT "$PID"
+      log "signal_sent signal=INT pid=${PID}"
+    fi
+    exit 0
+  fi
+  sleep "$INTERVAL_S"
+done
+
+log "watch_end pid=${PID} process_not_running"

bundle/code/openpi/scripts/train.py

@@ -0,0 +1,280 @@
+import dataclasses
+import functools
+import logging
+import platform
+from typing import Any
+
+import etils.epath as epath
+import flax.nnx as nnx
+from flax.training import common_utils
+import flax.traverse_util as traverse_util
+import jax
+import jax.experimental
+import jax.numpy as jnp
+import numpy as np
+import optax
+import tqdm_loggable.auto as tqdm
+import wandb
+
+import openpi.models.model as _model
+import openpi.shared.array_typing as at
+import openpi.shared.nnx_utils as nnx_utils
+import openpi.training.checkpoints as _checkpoints
+import openpi.training.config as _config
+import openpi.training.data_loader as _data_loader
+import openpi.training.optimizer as _optimizer
+import openpi.training.sharding as sharding
+import openpi.training.utils as training_utils
+import openpi.training.weight_loaders as _weight_loaders
+
+
+def init_logging():
+    """Custom logging format for better readability."""
+    level_mapping = {"DEBUG": "D", "INFO": "I", "WARNING": "W", "ERROR": "E", "CRITICAL": "C"}
+
+    class CustomFormatter(logging.Formatter):
+        def format(self, record):
+            record.levelname = level_mapping.get(record.levelname, record.levelname)
+            return super().format(record)
+
+    formatter = CustomFormatter(
+        fmt="%(asctime)s.%(msecs)03d [%(levelname)s] %(message)-80s (%(process)d:%(filename)s:%(lineno)s)",
+        datefmt="%H:%M:%S",
+    )
+
+    logger = logging.getLogger()
+    logger.setLevel(logging.INFO)
+    logger.handlers[0].setFormatter(formatter)
+
+
+def init_wandb(config: _config.TrainConfig, *, resuming: bool, log_code: bool = False, enabled: bool = True):
+    if not enabled:
+        wandb.init(mode="disabled")
+        return
+
+    ckpt_dir = config.checkpoint_dir
+    if not ckpt_dir.exists():
+        raise FileNotFoundError(f"Checkpoint directory {ckpt_dir} does not exist.")
+    if resuming:
+        run_id = (ckpt_dir / "wandb_id.txt").read_text().strip()
+        wandb.init(id=run_id, resume="must", project=config.project_name)
+    else:
+        wandb.init(
+            name=config.exp_name,
+            config=dataclasses.asdict(config),
+            project=config.project_name,
+        )
+        (ckpt_dir / "wandb_id.txt").write_text(wandb.run.id)
+
+    if log_code:
+        wandb.run.log_code(epath.Path(__file__).parent.parent)
+
+
+def _load_weights_and_validate(loader: _weight_loaders.WeightLoader, params_shape: at.Params) -> at.Params:
+    """Loads and validates the weights. Returns a loaded subset of the weights."""
+    loaded_params = loader.load(params_shape)
+    at.check_pytree_equality(expected=params_shape, got=loaded_params, check_shapes=True, check_dtypes=True)
+
+    # Remove jax.ShapeDtypeStruct from the loaded params. This makes sure that only the loaded params are returned.
+    return traverse_util.unflatten_dict(
+        {k: v for k, v in traverse_util.flatten_dict(loaded_params).items() if not isinstance(v, jax.ShapeDtypeStruct)}
+    )
+
+
+@at.typecheck
+def init_train_state(
+    config: _config.TrainConfig, init_rng: at.KeyArrayLike, mesh: jax.sharding.Mesh, *, resume: bool
+) -> tuple[training_utils.TrainState, Any]:
+    tx = _optimizer.create_optimizer(config.optimizer, config.lr_schedule, weight_decay_mask=None)
+
+    def init(rng: at.KeyArrayLike, partial_params: at.Params | None = None) -> training_utils.TrainState:
+        rng, model_rng = jax.random.split(rng)
+        # initialize the model (and its parameters).
+        model = config.model.create(model_rng)
+
+        # Merge the partial params into the model.
+        if partial_params is not None:
+            graphdef, state = nnx.split(model)
+            # This will produce an error if the partial params are not a subset of the state.
+            state.replace_by_pure_dict(partial_params)
+            model = nnx.merge(graphdef, state)
+
+        params = nnx.state(model)
+        # Convert frozen params to bfloat16.
+        params = nnx_utils.state_map(params, config.freeze_filter, lambda p: p.replace(p.value.astype(jnp.bfloat16)))
+
+        return training_utils.TrainState(
+            step=0,
+            params=params,
+            model_def=nnx.graphdef(model),
+            tx=tx,
+            opt_state=tx.init(params.filter(config.trainable_filter)),
+            ema_decay=config.ema_decay,
+            ema_params=None if config.ema_decay is None else params,
+        )
+
+    train_state_shape = jax.eval_shape(init, init_rng)
+    state_sharding = sharding.fsdp_sharding(train_state_shape, mesh, log=True)
+
+    if resume:
+        return train_state_shape, state_sharding
+
+    partial_params = _load_weights_and_validate(config.weight_loader, train_state_shape.params.to_pure_dict())
+    replicated_sharding = jax.sharding.NamedSharding(mesh, jax.sharding.PartitionSpec())
+
+    # Initialize the train state and mix in the partial params.
+    train_state = jax.jit(
+        init,
+        donate_argnums=(1,),  # donate the partial params buffer.
+        in_shardings=replicated_sharding,
+        out_shardings=state_sharding,
+    )(init_rng, partial_params)
+
+    return train_state, state_sharding
+
+
+@at.typecheck
+def train_step(
+    config: _config.TrainConfig,
+    rng: at.KeyArrayLike,
+    state: training_utils.TrainState,
+    batch: tuple[_model.Observation, _model.Actions],
+) -> tuple[training_utils.TrainState, dict[str, at.Array]]:
+    model = nnx.merge(state.model_def, state.params)
+    model.train()
+
+    @at.typecheck
+    def loss_fn(
+        model: _model.BaseModel, rng: at.KeyArrayLike, observation: _model.Observation, actions: _model.Actions
+    ):
+        chunked_loss = model.compute_loss(rng, observation, actions, train=True)
+        return jnp.mean(chunked_loss)
+
+    train_rng = jax.random.fold_in(rng, state.step)
+    observation, actions = batch
+
+    # Filter out frozen params.
+    diff_state = nnx.DiffState(0, config.trainable_filter)
+    loss, grads = nnx.value_and_grad(loss_fn, argnums=diff_state)(model, train_rng, observation, actions)
+
+    params = state.params.filter(config.trainable_filter)
+    updates, new_opt_state = state.tx.update(grads, state.opt_state, params)
+    new_params = optax.apply_updates(params, updates)
+
+    # Update the model in place and return the new full state.
+    nnx.update(model, new_params)
+    new_params = nnx.state(model)
+
+    new_state = dataclasses.replace(state, step=state.step + 1, params=new_params, opt_state=new_opt_state)
+    if state.ema_decay is not None:
+        new_state = dataclasses.replace(
+            new_state,
+            ema_params=jax.tree.map(
+                lambda old, new: state.ema_decay * old + (1 - state.ema_decay) * new, state.ema_params, new_params
+            ),
+        )
+
+    # Filter out params that aren't kernels.
+    kernel_params = nnx.state(
+        model,
+        nnx.All(
+            nnx.Param,
+            nnx.Not(nnx_utils.PathRegex(".*/(bias|scale|pos_embedding|input_embedding)")),
+            lambda _, x: x.value.ndim > 1,
+        ),
+    )
+    info = {
+        "loss": loss,
+        "grad_norm": optax.global_norm(grads),
+        "param_norm": optax.global_norm(kernel_params),
+    }
+    return new_state, info
+
+
+def main(config: _config.TrainConfig):
+    init_logging()
+    logging.info(f"Running on: {platform.node()}")
+
+    if config.batch_size % jax.device_count() != 0:
+        raise ValueError(
+            f"Batch size {config.batch_size} must be divisible by the number of devices {jax.device_count()}."
+        )
+
+    jax.config.update("jax_compilation_cache_dir", str(epath.Path("~/.cache/jax").expanduser()))
+
+    rng = jax.random.key(config.seed)
+    train_rng, init_rng = jax.random.split(rng)
+
+    mesh = sharding.make_mesh(config.fsdp_devices)
+    data_sharding = jax.sharding.NamedSharding(mesh, jax.sharding.PartitionSpec(sharding.DATA_AXIS))
+    replicated_sharding = jax.sharding.NamedSharding(mesh, jax.sharding.PartitionSpec())
+
+    checkpoint_manager, resuming = _checkpoints.initialize_checkpoint_dir(
+        config.checkpoint_dir,
+        keep_period=config.keep_period,
+        overwrite=config.overwrite,
+        resume=config.resume,
+    )
+    init_wandb(config, resuming=resuming, enabled=config.wandb_enabled)
+
+    data_loader = _data_loader.create_data_loader(
+        config,
+        sharding=data_sharding,
+        shuffle=True,
+    )
+    data_iter = iter(data_loader)
+    batch = next(data_iter)
+    logging.info(f"Initialized data loader:\n{training_utils.array_tree_to_info(batch)}")
+
+    # Log images from first batch to sanity check.
+    images_to_log = [
+        wandb.Image(np.concatenate([np.array(img[i]) for img in batch[0].images.values()], axis=1))
+        for i in range(min(5, len(next(iter(batch[0].images.values())))))
+    ]
+    wandb.log({"camera_views": images_to_log}, step=0)
+
+    train_state, train_state_sharding = init_train_state(config, init_rng, mesh, resume=resuming)
+    jax.block_until_ready(train_state)
+    logging.info(f"Initialized train state:\n{training_utils.array_tree_to_info(train_state.params)}")
+
+    if resuming:
+        train_state = _checkpoints.restore_state(checkpoint_manager, train_state, data_loader)
+
+    ptrain_step = jax.jit(
+        functools.partial(train_step, config),
+        in_shardings=(replicated_sharding, train_state_sharding, data_sharding),
+        out_shardings=(train_state_sharding, replicated_sharding),
+        donate_argnums=(1,),
+    )
+
+    start_step = int(train_state.step)
+    pbar = tqdm.tqdm(
+        range(start_step, config.num_train_steps),
+        initial=start_step,
+        total=config.num_train_steps,
+        dynamic_ncols=True,
+    )
+
+    infos = []
+    for step in pbar:
+        with sharding.set_mesh(mesh):
+            train_state, info = ptrain_step(train_rng, train_state, batch)
+        infos.append(info)
+        if step % config.log_interval == 0:
+            stacked_infos = common_utils.stack_forest(infos)
+            reduced_info = jax.device_get(jax.tree.map(jnp.mean, stacked_infos))
+            info_str = ", ".join(f"{k}={v:.4f}" for k, v in reduced_info.items())
+            pbar.write(f"Step {step}: {info_str}")
+            wandb.log(reduced_info, step=step)
+            infos = []
+        batch = next(data_iter)
+
+        if (step % config.save_interval == 0 and step > start_step) or step == config.num_train_steps - 1:
+            _checkpoints.save_state(checkpoint_manager, train_state, data_loader, step)
+
+    logging.info("Waiting for checkpoint manager to finish")
+    checkpoint_manager.wait_until_finished()
+
+
+if __name__ == "__main__":
+    main(_config.cli())

bundle/code/openpi/scripts/watch_teddybear_train.sh

@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+set -uo pipefail
+
+if [[ $# -lt 4 ]]; then
+  echo "usage: $0 <pid> <train_log> <checkpoint_dir> <status_log> [interval_s]" >&2
+  exit 1
+fi
+
+PID="$1"
+TRAIN_LOG="$2"
+CHECKPOINT_DIR="$3"
+STATUS_LOG="$4"
+INTERVAL_S="${5:-60}"
+
+mkdir -p "$(dirname "$STATUS_LOG")"
+
+latest_step_line() {
+  grep -oE 'Step [0-9]+:.*' "$TRAIN_LOG" | tail -n 1 || true
+}
+
+latest_gpu_line() {
+  nvidia-smi \
+    --query-gpu=utilization.gpu,utilization.memory,memory.used,memory.free \
+    --format=csv,noheader,nounits | head -n 1 || true
+}
+
+latest_ckpts() {
+  find "$CHECKPOINT_DIR" -maxdepth 1 -mindepth 1 -type d -printf '%f\n' 2>/dev/null \
+    | sort -n \
+    | tail -n 5 \
+    | paste -sd ',' -
+}
+
+printf 'watch start %s pid=%s\n' "$(date -u +%FT%TZ)" "$PID" >> "$STATUS_LOG"
+
+while kill -0 "$PID" 2>/dev/null; do
+  STEP_LINE="$(latest_step_line || true)"
+  GPU_LINE="$(latest_gpu_line || true)"
+  CKPTS="$(latest_ckpts || true)"
+  printf '%s | %s | gpu=%s | ckpts=%s\n' \
+    "$(date -u +%FT%TZ)" \
+    "${STEP_LINE:-no_step_yet}" \
+    "${GPU_LINE:-na}" \
+    "${CKPTS:-none}" >> "$STATUS_LOG"
+  sleep "$INTERVAL_S" || true
+done
+
+printf 'watch end %s pid=%s\n' "$(date -u +%FT%TZ)" "$PID" >> "$STATUS_LOG"
+tail -n 40 "$TRAIN_LOG" >> "$STATUS_LOG" || true

bundle/code/openpi/src/openpi/training/config.py

@@ -0,0 +1,1036 @@
+"""See _CONFIGS for the list of available configs."""
+
+import abc
+from collections.abc import Sequence
+import dataclasses
+import difflib
+import logging
+import pathlib
+from typing import Any, Literal, Protocol, TypeAlias
+
+import etils.epath as epath
+import flax.nnx as nnx
+from typing_extensions import override
+import tyro
+
+import openpi.models.model as _model
+import openpi.models.pi0_config as pi0_config
+import openpi.models.pi0_fast as pi0_fast
+import openpi.models.tokenizer as _tokenizer
+import openpi.policies.aloha_policy as aloha_policy
+import openpi.policies.droid_policy as droid_policy
+import openpi.policies.libero_policy as libero_policy
+import openpi.shared.download as _download
+import openpi.shared.normalize as _normalize
+import openpi.training.droid_rlds_dataset as droid_rlds_dataset
+import openpi.training.misc.polaris_config as polaris_config
+import openpi.training.misc.roboarena_config as roboarena_config
+import openpi.training.optimizer as _optimizer
+import openpi.training.weight_loaders as weight_loaders
+import openpi.transforms as _transforms
+
+ModelType: TypeAlias = _model.ModelType
+# Work around a tyro issue with using nnx.filterlib.Filter directly.
+Filter: TypeAlias = nnx.filterlib.Filter
+
+
+@dataclasses.dataclass(frozen=True)
+class AssetsConfig:
+    """Determines the location of assets (e.g., norm stats) that will be used to set up the data pipeline.
+
+    These assets will be replicated inside the checkpoint under the `assets/asset_id` directory.
+
+    This can be used to load assets from a different checkpoint (e.g., base model checkpoint) or some other
+    centralized location. For example, to load the norm stats for the Trossen robot from the base model checkpoint
+    during fine-tuning, use:
+
+    ```
+    AssetsConfig(
+        assets_dir="gs://openpi-assets/checkpoints/pi0_base/assets",
+        asset_id="trossen",
+    )
+    ```
+    """
+
+    # Assets directory. If not provided, the config assets_dirs will be used. This is useful to load assets from
+    # a different checkpoint (e.g., base model checkpoint) or some other centralized location.
+    assets_dir: str | None = None
+
+    # Asset id. If not provided, the repo id will be used. This allows users to reference assets that describe
+    # different robot platforms.
+    asset_id: str | None = None
+
+
+@dataclasses.dataclass(frozen=True)
+class DataConfig:
+    # LeRobot repo id. If None, fake data will be created.
+    repo_id: str | None = None
+    # Directory within the assets directory containing the data assets.
+    asset_id: str | None = None
+    # Contains precomputed normalization stats. If None, normalization will not be performed.
+    norm_stats: dict[str, _transforms.NormStats] | None = None
+
+    # Used to adopt the inputs from a dataset specific format to a common format
+    # which is expected by the data transforms.
+    repack_transforms: _transforms.Group = dataclasses.field(default_factory=_transforms.Group)
+    # Data transforms, typically include robot specific transformations. Will be applied
+    # before the data is normalized. See `model.Observation` and `model.Actions` to learn about the
+    # normalized data.
+    data_transforms: _transforms.Group = dataclasses.field(default_factory=_transforms.Group)
+    # Model specific transforms. Will be applied after the data is normalized.
+    model_transforms: _transforms.Group = dataclasses.field(default_factory=_transforms.Group)
+    # If true, will use quantile normalization. Otherwise, normal z-score normalization will be used.
+    use_quantile_norm: bool = False
+
+    # Names of keys that will be used by the data loader to generate the action sequence. The length of the
+    # sequence is defined by the `action_horizon` field in the model config. This should be adjusted if your
+    # LeRobot dataset is using different keys to represent the action.
+    action_sequence_keys: Sequence[str] = ("actions",)
+
+    # If true, will use the LeRobot dataset task to define the prompt.
+    prompt_from_task: bool = False
+
+    # Only used for RLDS data loader (ie currently only used for DROID).
+    rlds_data_dir: str | None = None
+    # Action space for DROID dataset.
+    action_space: droid_rlds_dataset.DroidActionSpace | None = None
+    # List of datasets to sample from: name, version, weight, and optionally filter_dict_path
+    datasets: Sequence[droid_rlds_dataset.RLDSDataset] = ()
+
+
+class GroupFactory(Protocol):
+    def __call__(self, model_config: _model.BaseModelConfig) -> _transforms.Group:
+        """Create a group."""
+
+
+@dataclasses.dataclass(frozen=True)
+class ModelTransformFactory(GroupFactory):
+    """Creates model transforms for standard pi0 models."""
+
+    # If provided, will determine the default prompt that be used by the model.
+    default_prompt: str | None = None
+
+    def __call__(self, model_config: _model.BaseModelConfig) -> _transforms.Group:
+        match model_config.model_type:
+            case _model.ModelType.PI0:
+                return _transforms.Group(
+                    inputs=[
+                        _transforms.InjectDefaultPrompt(self.default_prompt),
+                        _transforms.ResizeImages(224, 224),
+                        _transforms.TokenizePrompt(
+                            _tokenizer.PaligemmaTokenizer(model_config.max_token_len),
+                        ),
+                        _transforms.PadStatesAndActions(model_config.action_dim),
+                    ],
+                )
+            case _model.ModelType.PI05:
+                assert isinstance(model_config, pi0_config.Pi0Config)
+                return _transforms.Group(
+                    inputs=[
+                        _transforms.InjectDefaultPrompt(self.default_prompt),
+                        _transforms.ResizeImages(224, 224),
+                        _transforms.TokenizePrompt(
+                            _tokenizer.PaligemmaTokenizer(model_config.max_token_len),
+                            discrete_state_input=model_config.discrete_state_input,
+                        ),
+                        _transforms.PadStatesAndActions(model_config.action_dim),
+                    ],
+                )
+            case _model.ModelType.PI0_FAST:
+                tokenizer_cls = (
+                    _tokenizer.FASTTokenizer
+                    if model_config.fast_model_tokenizer is None
+                    else model_config.fast_model_tokenizer
+                )
+                tokenizer_kwargs = (
+                    {} if model_config.fast_model_tokenizer_kwargs is None else model_config.fast_model_tokenizer_kwargs
+                )
+                return _transforms.Group(
+                    inputs=[
+                        _transforms.InjectDefaultPrompt(self.default_prompt),
+                        _transforms.ResizeImages(224, 224),
+                        _transforms.TokenizeFASTInputs(
+                            tokenizer_cls(model_config.max_token_len, **tokenizer_kwargs),
+                        ),
+                    ],
+                    outputs=[
+                        _transforms.ExtractFASTActions(
+                            tokenizer_cls(model_config.max_token_len, **tokenizer_kwargs),
+                            action_horizon=model_config.action_horizon,
+                            action_dim=model_config.action_dim,
+                        )
+                    ],
+                )
+
+
+@dataclasses.dataclass(frozen=True)
+class DataConfigFactory(abc.ABC):
+    # The LeRobot repo id.
+    repo_id: str = tyro.MISSING
+    # Determines how the assets will be loaded.
+    assets: AssetsConfig = dataclasses.field(default_factory=AssetsConfig)
+    # Base config that will be updated by the factory.
+    base_config: tyro.conf.Suppress[DataConfig | None] = None
+
+    @abc.abstractmethod
+    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        """Create a data config."""
+
+    def create_base_config(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        repo_id = self.repo_id if self.repo_id is not tyro.MISSING else None
+        asset_id = self.assets.asset_id or repo_id
+        return dataclasses.replace(
+            self.base_config or DataConfig(),
+            repo_id=repo_id,
+            asset_id=asset_id,
+            norm_stats=self._load_norm_stats(epath.Path(self.assets.assets_dir or assets_dirs), asset_id),
+            use_quantile_norm=model_config.model_type != ModelType.PI0,
+        )
+
+    def _load_norm_stats(self, assets_dir: epath.Path, asset_id: str | None) -> dict[str, _transforms.NormStats] | None:
+        if asset_id is None:
+            return None
+        try:
+            data_assets_dir = str(assets_dir / asset_id)
+            norm_stats = _normalize.load(_download.maybe_download(data_assets_dir))
+            logging.info(f"Loaded norm stats from {data_assets_dir}")
+            return norm_stats
+        except FileNotFoundError:
+            logging.info(f"Norm stats not found in {data_assets_dir}, skipping.")
+        return None
+
+
+@dataclasses.dataclass(frozen=True)
+class FakeDataConfig(DataConfigFactory):
+    repo_id: str = "fake"
+
+    @override
+    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        return DataConfig(repo_id=self.repo_id)
+
+
+@dataclasses.dataclass(frozen=True)
+class SimpleDataConfig(DataConfigFactory):
+    # Factory for the data transforms.
+    data_transforms: tyro.conf.Suppress[GroupFactory] = dataclasses.field(default_factory=GroupFactory)
+    # Factory for the model transforms.
+    model_transforms: tyro.conf.Suppress[GroupFactory] = dataclasses.field(default_factory=ModelTransformFactory)
+
+    @override
+    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        return dataclasses.replace(
+            self.create_base_config(assets_dirs, model_config),
+            data_transforms=self.data_transforms(model_config),
+            model_transforms=self.model_transforms(model_config),
+        )
+
+
+@dataclasses.dataclass(frozen=True)
+class LeRobotAlohaDataConfig(DataConfigFactory):
+    # If true, will convert joint dimensions to deltas with respect to the current state before passing to the model.
+    # Gripper dimensions will remain in absolute values.
+    use_delta_joint_actions: bool = True
+    # If provided, will be injected into the input data if the "prompt" key is not present.
+    default_prompt: str | None = None
+    # If true, this will convert the joint and gripper values from the standard Aloha space to
+    # the space used by the pi internal runtime which was used to train the base model. People who
+    # use standard Aloha data should set this to true.
+    adapt_to_pi: bool = True
+
+    # Repack transforms.
+    repack_transforms: tyro.conf.Suppress[_transforms.Group] = dataclasses.field(
+        default=_transforms.Group(
+            inputs=[
+                _transforms.RepackTransform(
+                    {
+                        "images": {"cam_high": "observation.images.top"},
+                        "state": "observation.state",
+                        "actions": "action",
+                    }
+                )
+            ]
+        )
+    )
+    # Action keys that will be used to read the action sequence from the dataset.
+    action_sequence_keys: Sequence[str] = ("action",)
+
+    @override
+    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        data_transforms = _transforms.Group(
+            inputs=[aloha_policy.AlohaInputs(adapt_to_pi=self.adapt_to_pi)],
+            outputs=[aloha_policy.AlohaOutputs(adapt_to_pi=self.adapt_to_pi)],
+        )
+        if self.use_delta_joint_actions:
+            delta_action_mask = _transforms.make_bool_mask(6, -1, 6, -1)
+            data_transforms = data_transforms.push(
+                inputs=[_transforms.DeltaActions(delta_action_mask)],
+                outputs=[_transforms.AbsoluteActions(delta_action_mask)],
+            )
+
+        model_transforms = ModelTransformFactory(default_prompt=self.default_prompt)(model_config)
+
+        return dataclasses.replace(
+            self.create_base_config(assets_dirs, model_config),
+            repack_transforms=self.repack_transforms,
+            data_transforms=data_transforms,
+            model_transforms=model_transforms,
+            action_sequence_keys=self.action_sequence_keys,
+        )
+
+
+@dataclasses.dataclass(frozen=True)
+class LeRobotLiberoDataConfig(DataConfigFactory):
+    """
+    This config is used to configure transforms that are applied at various parts of the data pipeline.
+    For your own dataset, you can copy this class and modify the transforms to match your dataset based on the
+    comments below.
+    """
+
+    extra_delta_transform: bool = False
+
+    @override
+    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        # The repack transform is *only* applied to the data coming from the dataset,
+        # and *not* during inference. We can use it to make inputs from the dataset look
+        # as close as possible to those coming from the inference environment (e.g. match the keys).
+        # Below, we match the keys in the dataset (which we defined in the data conversion script) to
+        # the keys we use in our inference pipeline (defined in the inference script for libero).
+        # For your own dataset, first figure out what keys your environment passes to the policy server
+        # and then modify the mappings below so your dataset's keys get matched to those target keys.
+        # The repack transform simply remaps key names here.
+        repack_transform = _transforms.Group(
+            inputs=[
+                _transforms.RepackTransform(
+                    {
+                        "observation/image": "image",
+                        "observation/wrist_image": "wrist_image",
+                        "observation/state": "state",
+                        "actions": "actions",
+                        "prompt": "prompt",
+                    }
+                )
+            ]
+        )
+
+        # The data transforms are applied to the data coming from the dataset *and* during inference.
+        # Below, we define the transforms for data going into the model (``inputs``) and the transforms
+        # for data coming out of the model (``outputs``) (the latter is only used during inference).
+        # We defined these transforms in `libero_policy.py`. You can check the detailed comments there for
+        # how to modify the transforms to match your dataset. Once you created your own transforms, you can
+        # replace the transforms below with your own.
+        data_transforms = _transforms.Group(
+            inputs=[libero_policy.LiberoInputs(model_type=model_config.model_type)],
+            outputs=[libero_policy.LiberoOutputs()],
+        )
+
+        # One additional data transform: pi0 models are trained on delta actions (relative to the first
+        # state in each action chunk). IF your data has ``absolute`` actions (e.g. target joint angles)
+        # you can uncomment the following line to convert the actions to delta actions. The only exception
+        # is for the gripper actions which are always absolute.
+        # In the example below, we would apply the delta conversion to the first 6 actions (joints) and
+        # leave the 7th action (gripper) unchanged, i.e. absolute.
+        # In Libero, the raw actions in the dataset are already delta actions, so we *do not* need to
+        # apply a separate delta conversion (that's why it's commented out). Choose whether to apply this
+        # transform based on whether your dataset uses ``absolute`` or ``delta`` actions out of the box.
+
+        # LIBERO already represents actions as deltas, but we have some old Pi0 checkpoints that are trained with this
+        # extra delta transform.
+        if self.extra_delta_transform:
+            delta_action_mask = _transforms.make_bool_mask(6, -1)
+            data_transforms = data_transforms.push(
+                inputs=[_transforms.DeltaActions(delta_action_mask)],
+                outputs=[_transforms.AbsoluteActions(delta_action_mask)],
+            )
+
+        # Model transforms include things like tokenizing the prompt and action targets
+        # You do not need to change anything here for your own dataset.
+        model_transforms = ModelTransformFactory()(model_config)
+
+        # We return all data transforms for training and inference. No need to change anything here.
+        return dataclasses.replace(
+            self.create_base_config(assets_dirs, model_config),
+            repack_transforms=repack_transform,
+            data_transforms=data_transforms,
+            model_transforms=model_transforms,
+        )
+
+
+@dataclasses.dataclass(frozen=True)
+class RLDSDroidDataConfig(DataConfigFactory):
+    """
+    Config for training on DROID, using RLDS data format (for efficient training on larger datasets).
+    """
+
+    rlds_data_dir: str | None = None
+    action_space: droid_rlds_dataset.DroidActionSpace | None = None
+
+    # Filtering options. Can pass a path to a dictionary that maps episodes to timestep ranges
+    # to tuples denoting ranges of time steps to keep (start, end). Episodes are uniquely identified with
+    # f"{recording_folderpath}--{file_path}", both of which are present in the RLDS episode metadata.
+
+    # List of datasets to sample from: name, version, weight, and optionally filter_dict_path
+    datasets: Sequence[droid_rlds_dataset.RLDSDataset] = (
+        droid_rlds_dataset.RLDSDataset(
+            name="droid",
+            version="1.0.1",
+            weight=1.0,
+            filter_dict_path="gs://openpi-assets/droid/droid_sample_ranges_v1_0_1.json",
+        ),
+    )
+
+    @override
+    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        repack_transform = _transforms.Group(
+            inputs=[
+                _transforms.RepackTransform(
+                    {
+                        "observation/exterior_image_1_left": "observation/image",
+                        "observation/wrist_image_left": "observation/wrist_image",
+                        "observation/joint_position": "observation/joint_position",
+                        "observation/gripper_position": "observation/gripper_position",
+                        "actions": "actions",
+                        "prompt": "prompt",
+                    }
+                )
+            ]
+        )
+
+        data_transforms = _transforms.Group(
+            inputs=[droid_policy.DroidInputs(model_type=model_config.model_type)],
+            outputs=[droid_policy.DroidOutputs()],
+        )
+
+        if self.action_space == droid_rlds_dataset.DroidActionSpace.JOINT_POSITION:
+            # Data loader returns absolute joint position actions -- convert to delta actions for training.
+            delta_action_mask = _transforms.make_bool_mask(7, -1)
+            data_transforms = data_transforms.push(
+                inputs=[_transforms.DeltaActions(delta_action_mask)],
+                outputs=[_transforms.AbsoluteActions(delta_action_mask)],
+            )
+
+        model_transforms = ModelTransformFactory()(model_config)
+
+        assert self.rlds_data_dir is not None, "Need to set rlds data dir for RLDS data loader."
+
+        return dataclasses.replace(
+            self.create_base_config(assets_dirs, model_config),
+            repack_transforms=repack_transform,
+            data_transforms=data_transforms,
+            model_transforms=model_transforms,
+            rlds_data_dir=self.rlds_data_dir,
+            action_space=self.action_space,
+            datasets=self.datasets,
+        )
+
+
+@dataclasses.dataclass(frozen=True)
+class LeRobotDROIDDataConfig(DataConfigFactory):
+    """
+    Example data config for custom DROID dataset in LeRobot format.
+    To convert your custom DROID dataset (<10s of hours) to LeRobot format, see examples/droid/convert_droid_data_to_lerobot.py
+    """
+
+    @override
+    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
+        repack_transform = _transforms.Group(
+            inputs=[
+                _transforms.RepackTransform(
+                    {
+                        "observation/exterior_image_1_left": "exterior_image_1_left",
+                        "observation/exterior_image_2_left": "exterior_image_2_left",
+                        "observation/wrist_image_left": "wrist_image_left",
+                        "observation/joint_position": "joint_position",
+                        "observation/gripper_position": "gripper_position",
+                        "actions": "actions",
+                        "prompt": "prompt",
+                    }
+                )
+            ]
+        )
+        # We assume joint *velocity* actions, so we should *not* apply an additional delta transform.
+        data_transforms = _transforms.Group(
+            inputs=[droid_policy.DroidInputs(model_type=model_config.model_type)],
+            outputs=[droid_policy.DroidOutputs()],
+        )
+        model_transforms = ModelTransformFactory()(model_config)
+
+        return dataclasses.replace(
+            self.create_base_config(assets_dirs, model_config),
+            repack_transforms=repack_transform,
+            data_transforms=data_transforms,
+            model_transforms=model_transforms,
+        )
+
+
+@dataclasses.dataclass(frozen=True)
+class TrainConfig:
+    # Name of the config. Must be unique. Will be used to reference this config.
+    name: tyro.conf.Suppress[str]
+    # Project name.
+    project_name: str = "openpi"
+    # Experiment name. Will be used to name the metadata and checkpoint directories.
+    exp_name: str = tyro.MISSING
+
+    # Defines the model config. Some attributes (action_dim, action_horizon, and max_token_len) are shared by all models
+    # -- see BaseModelConfig. Specific model implementations (e.g., Pi0Config) inherit from BaseModelConfig and may
+    # define additional attributes.
+    model: _model.BaseModelConfig = dataclasses.field(default_factory=pi0_config.Pi0Config)
+
+    # A weight loader can optionally load (possibly partial) weights from disk after the model is initialized.
+    weight_loader: weight_loaders.WeightLoader = dataclasses.field(default_factory=weight_loaders.NoOpWeightLoader)
+
+    # Optional path to a PyTorch checkpoint to load weights from.
+    pytorch_weight_path: str | None = None
+
+    # Precision for PyTorch training.
+    pytorch_training_precision: Literal["bfloat16", "float32"] = "bfloat16"
+
+    lr_schedule: _optimizer.LRScheduleConfig = dataclasses.field(default_factory=_optimizer.CosineDecaySchedule)
+    optimizer: _optimizer.OptimizerConfig = dataclasses.field(default_factory=_optimizer.AdamW)
+    ema_decay: float | None = 0.99
+
+    # Specifies which weights should be frozen.
+    freeze_filter: tyro.conf.Suppress[Filter] = dataclasses.field(default_factory=nnx.Nothing)
+
+    # Determines the data to be trained on.
+    data: DataConfigFactory = dataclasses.field(default_factory=FakeDataConfig)
+
+    # Base directory for config assets (e.g., norm stats).
+    assets_base_dir: str = "./assets"
+    # Base directory for checkpoints.
+    checkpoint_base_dir: str = "./checkpoints"
+
+    # Random seed that will be used by random generators during training.
+    seed: int = 42
+    # Global batch size.
+    batch_size: int = 32
+    # Number of workers to use for the data loader. Increasing this number will speed up data loading but
+    # will increase memory and CPU usage.
+    num_workers: int = 2
+    # Number of train steps (batches) to run.
+    num_train_steps: int = 30_000
+
+    # How often (in steps) to log training metrics.
+    log_interval: int = 100
+    # How often (in steps) to save checkpoints.
+    save_interval: int = 1000
+    # If set, any existing checkpoints matching step % keep_period == 0 will not be deleted.
+    keep_period: int | None = 5000
+
+    # If true, will overwrite the checkpoint directory if it already exists.
+    overwrite: bool = False
+    # If true, will resume training from the last checkpoint.
+    resume: bool = False
+
+    # If true, will enable wandb logging.
+    wandb_enabled: bool = True
+
+    # Used to pass metadata to the policy server.
+    policy_metadata: dict[str, Any] | None = None
+
+    # If the value is greater than 1, FSDP will be enabled and shard across number of specified devices; overall
+    # device memory will be reduced but training could potentially be slower.
+    # eg. if total device is 4 and fsdp devices is 2; then the model will shard to 2 devices and run
+    # data parallel between 2 groups of devices.
+    fsdp_devices: int = 1
+
+    @property
+    def assets_dirs(self) -> pathlib.Path:
+        """Get the assets directory for this config."""
+        return (pathlib.Path(self.assets_base_dir) / self.name).resolve()
+
+    @property
+    def checkpoint_dir(self) -> pathlib.Path:
+        """Get the checkpoint directory for this config."""
+        if not self.exp_name:
+            raise ValueError("--exp_name must be set")
+        return (pathlib.Path(self.checkpoint_base_dir) / self.name / self.exp_name).resolve()
+
+    @property
+    def trainable_filter(self) -> nnx.filterlib.Filter:
+        """Get the filter for the trainable parameters."""
+        return nnx.All(nnx.Param, nnx.Not(self.freeze_filter))
+
+    def __post_init__(self) -> None:
+        if self.resume and self.overwrite:
+            raise ValueError("Cannot resume and overwrite at the same time.")
+
+
+# Use `get_config` if you need to get a config by name in your code.
+_CONFIGS = [
+    #
+    # Inference Aloha configs.
+    #
+    TrainConfig(
+        name="pi0_aloha",
+        model=pi0_config.Pi0Config(),
+        data=LeRobotAlohaDataConfig(
+            assets=AssetsConfig(asset_id="trossen"),
+        ),
+        policy_metadata={"reset_pose": [0, -1.5, 1.5, 0, 0, 0]},
+    ),
+    TrainConfig(
+        name="pi05_aloha",
+        model=pi0_config.Pi0Config(pi05=True),
+        data=LeRobotAlohaDataConfig(
+            assets=AssetsConfig(asset_id="trossen"),
+        ),
+        policy_metadata={"reset_pose": [0, -1.5, 1.5, 0, 0, 0]},
+    ),
+    TrainConfig(
+        name="pi0_aloha_towel",
+        model=pi0_config.Pi0Config(),
+        data=LeRobotAlohaDataConfig(
+            assets=AssetsConfig(asset_id="trossen"),
+            default_prompt="fold the towel",
+        ),
+        policy_metadata={"reset_pose": [0, -1.5, 1.5, 0, 0, 0]},
+    ),
+    TrainConfig(
+        name="pi0_aloha_tupperware",
+        model=pi0_config.Pi0Config(),
+        data=LeRobotAlohaDataConfig(
+            assets=AssetsConfig(asset_id="trossen"),
+            default_prompt="open the tupperware and put the food on the plate",
+        ),
+        policy_metadata={"reset_pose": [0, -1.5, 1.5, 0, 0, 0]},
+    ),
+    #
+    # Inference DROID configs.
+    #
+    TrainConfig(
+        name="pi0_droid",
+        model=pi0_config.Pi0Config(action_horizon=10),
+        data=SimpleDataConfig(
+            assets=AssetsConfig(asset_id="droid"),
+            data_transforms=lambda model: _transforms.Group(
+                inputs=[droid_policy.DroidInputs(model_type=ModelType.PI0)],
+                outputs=[droid_policy.DroidOutputs()],
+            ),
+            base_config=DataConfig(
+                prompt_from_task=True,
+            ),
+        ),
+    ),
+    TrainConfig(
+        name="pi0_fast_droid",
+        model=pi0_fast.Pi0FASTConfig(action_dim=8, action_horizon=10),
+        data=SimpleDataConfig(
+            assets=AssetsConfig(asset_id="droid"),
+            data_transforms=lambda model: _transforms.Group(
+                inputs=[droid_policy.DroidInputs(model_type=ModelType.PI0_FAST)],
+                outputs=[droid_policy.DroidOutputs()],
+            ),
+            base_config=DataConfig(
+                prompt_from_task=True,
+            ),
+        ),
+    ),
+    TrainConfig(
+        name="pi05_droid",
+        model=pi0_config.Pi0Config(action_horizon=15, pi05=True),
+        data=SimpleDataConfig(
+            assets=AssetsConfig(asset_id="droid"),
+            data_transforms=lambda model: _transforms.Group(
+                inputs=[droid_policy.DroidInputs(model_type=ModelType.PI05)],
+                outputs=[droid_policy.DroidOutputs()],
+            ),
+            base_config=DataConfig(
+                prompt_from_task=True,
+            ),
+        ),
+    ),
+    #
+    # Fine-tuning Libero configs.
+    #
+    # These train configs define the hyperparameters for fine-tuning the base model on your own dataset.
+    # They are used to define key elements like the dataset you are training on, the base checkpoint you
+    # are using, and other hyperparameters like how many training steps to run or what learning rate to use.
+    # For your own dataset, you can copy this class and modify the dataset name, and data transforms based on
+    # the comments below.
+    TrainConfig(
+        # Change the name to reflect your model and dataset.
+        name="pi0_libero",
+        # Here you define the model config -- In this example we use pi0 as the model
+        # architecture and perform *full* finetuning. in the examples below we show how to modify
+        # this to perform *low-memory* (LORA) finetuning and use pi0-FAST as an alternative architecture.
+        model=pi0_config.Pi0Config(),
+        # Here you define the dataset you are training on. In this example we use the Libero
+        # dataset. For your own dataset, you can change the repo_id to point to your dataset.
+        # Also modify the DataConfig to use the new config you made for your dataset above.
+        data=LeRobotLiberoDataConfig(
+            repo_id="physical-intelligence/libero",
+            base_config=DataConfig(
+                # This flag determines whether we load the prompt (i.e. the task instruction) from the
+                # ``task`` field in the LeRobot dataset. If set to True, the prompt will show up in
+                # a field called ``prompt`` in the input dict. The recommended setting is True.
+                prompt_from_task=True,
+            ),
+            extra_delta_transform=True,
+        ),
+        # Here you define which pre-trained checkpoint you want to load to initialize the model.
+        # This should match the model config you chose above -- i.e. in this case we use the pi0 base model.
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"),
+        # Below you can define other hyperparameters like the learning rate, number of training steps, etc.
+        # Check the base TrainConfig class for a full list of available hyperparameters.
+        num_train_steps=30_000,
+    ),
+    TrainConfig(
+        name="pi0_libero_low_mem_finetune",
+        # Here is an example of loading a pi0 model for LoRA fine-tuning.
+        model=pi0_config.Pi0Config(paligemma_variant="gemma_2b_lora", action_expert_variant="gemma_300m_lora"),
+        data=LeRobotLiberoDataConfig(
+            repo_id="physical-intelligence/libero",
+            base_config=DataConfig(prompt_from_task=True),
+            extra_delta_transform=True,
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"),
+        num_train_steps=30_000,
+        # The freeze filter defines which parameters should be frozen during training.
+        # We have a convenience function in the model config that returns the default freeze filter
+        # for the given model config for LoRA finetuning. Just make sure it matches the model config
+        # you chose above.
+        freeze_filter=pi0_config.Pi0Config(
+            paligemma_variant="gemma_2b_lora", action_expert_variant="gemma_300m_lora"
+        ).get_freeze_filter(),
+        # Turn off EMA for LoRA finetuning.
+        ema_decay=None,
+    ),
+    TrainConfig(
+        name="pi0_fast_libero",
+        # Here is an example of loading a pi0-FAST model for full finetuning.
+        # Modify action_dim and action_horizon to match your dataset (action horizon is equal to
+        # the desired action chunk length).
+        # The max_token_len is the maximum number of (non-image) tokens the model can handle.
+        # This includes the tokenized prompt, proprioceptive state, and (FAST-tokenized) action tokens.
+        # Choosing this value too small may chop off tokens at the end of your sequence (the code will throw
+        # a warning), while choosing it too large will waste memory (since we pad each batch element to the
+        # max_token_len). A good rule of thumb is to use approx 180 for single-arm robots, and approx 250 for
+        # two-arm robots. Generally, err on the lower side here first, and potentially increase the value if
+        # you see many warnings being thrown during training.
+        model=pi0_fast.Pi0FASTConfig(action_dim=7, action_horizon=10, max_token_len=180),
+        data=LeRobotLiberoDataConfig(
+            repo_id="physical-intelligence/libero",
+            base_config=DataConfig(prompt_from_task=True),
+            extra_delta_transform=True,
+        ),
+        # Note that we load the pi0-FAST base model checkpoint here.
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_fast_base/params"),
+        num_train_steps=30_000,
+    ),
+    TrainConfig(
+        name="pi0_fast_libero_low_mem_finetune",
+        # Here is an example of loading a pi0-FAST model for LoRA finetuning.
+        # For setting action_dim, action_horizon, and max_token_len, see the comments above.
+        model=pi0_fast.Pi0FASTConfig(
+            action_dim=7, action_horizon=10, max_token_len=180, paligemma_variant="gemma_2b_lora"
+        ),
+        data=LeRobotLiberoDataConfig(
+            repo_id="physical-intelligence/libero",
+            base_config=DataConfig(prompt_from_task=True),
+            extra_delta_transform=True,
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_fast_base/params"),
+        num_train_steps=30_000,
+        # Again, make sure to match the model config above when extracting the freeze filter
+        # that specifies which parameters should be frozen during LoRA finetuning.
+        freeze_filter=pi0_fast.Pi0FASTConfig(
+            action_dim=7, action_horizon=10, max_token_len=180, paligemma_variant="gemma_2b_lora"
+        ).get_freeze_filter(),
+        # Turn off EMA for LoRA finetuning.
+        ema_decay=None,
+    ),
+    TrainConfig(
+        name="pi05_libero",
+        model=pi0_config.Pi0Config(pi05=True, action_horizon=10, discrete_state_input=False),
+        data=LeRobotLiberoDataConfig(
+            repo_id="physical-intelligence/libero",
+            base_config=DataConfig(prompt_from_task=True),
+            extra_delta_transform=False,
+        ),
+        batch_size=256,
+        lr_schedule=_optimizer.CosineDecaySchedule(
+            warmup_steps=10_000,
+            peak_lr=5e-5,
+            decay_steps=1_000_000,
+            decay_lr=5e-5,
+        ),
+        optimizer=_optimizer.AdamW(clip_gradient_norm=1.0),
+        ema_decay=0.999,
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+        pytorch_weight_path="/path/to/your/pytorch_weight_path",
+        num_train_steps=30_000,
+    ),
+    TrainConfig(
+        name="pi05_kinova_teddybear",
+        model=pi0_config.Pi0Config(
+            pi05=True,
+            action_dim=32,
+            action_horizon=16,
+        ),
+        data=SimpleDataConfig(
+            repo_id="lsnu/TeddyBearKinovaTestSetLeRobot",
+            base_config=DataConfig(
+                prompt_from_task=False,
+                repack_transforms=_transforms.Group(
+                    inputs=[
+                        _transforms.RepackTransform(
+                            {
+                                "observation/image": "observation.images.azure_rgb",
+                                "observation/wrist_image": "observation.images.wrist",
+                                "observation/state": "observation.state",
+                                "actions": "action",
+                            }
+                        )
+                    ]
+                ),
+                action_sequence_keys=("action",),
+            ),
+            data_transforms=lambda model: _transforms.Group(
+                inputs=[libero_policy.LiberoInputs(model_type=model.model_type)],
+                outputs=[libero_policy.LiberoOutputs()],
+            ).push(
+                # The dataset stores absolute next-step end-effector poses.
+                # Convert the 6 pose dimensions to deltas and keep gripper absolute.
+                inputs=[_transforms.DeltaActions(_transforms.make_bool_mask(6, -1))],
+                outputs=[_transforms.AbsoluteActions(_transforms.make_bool_mask(6, -1))],
+            ),
+            model_transforms=ModelTransformFactory(
+                default_prompt="pick up the teddy bear and place it in the red box",
+            ),
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+        batch_size=32,
+        num_workers=32,
+        num_train_steps=5_000,
+        log_interval=25,
+        save_interval=500,
+        keep_period=2_000,
+        wandb_enabled=False,
+    ),
+    #
+    # Fine-tuning Aloha configs.
+    #
+    # This is a test config that is used to illustate how train on a custom LeRobot dataset.
+    # For instructions on how to convert and train on your own Aloha dataset see examples/aloha_real/README.md
+    TrainConfig(
+        name="pi0_aloha_pen_uncap",
+        model=pi0_config.Pi0Config(),
+        data=LeRobotAlohaDataConfig(
+            repo_id="physical-intelligence/aloha_pen_uncap_diverse",
+            assets=AssetsConfig(
+                assets_dir="gs://openpi-assets/checkpoints/pi0_base/assets",
+                asset_id="trossen",
+            ),
+            default_prompt="uncap the pen",
+            repack_transforms=_transforms.Group(
+                inputs=[
+                    _transforms.RepackTransform(
+                        {
+                            "images": {
+                                "cam_high": "observation.images.cam_high",
+                                "cam_left_wrist": "observation.images.cam_left_wrist",
+                                "cam_right_wrist": "observation.images.cam_right_wrist",
+                            },
+                            "state": "observation.state",
+                            "actions": "action",
+                        }
+                    )
+                ]
+            ),
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"),
+        num_train_steps=20_000,
+    ),
+    TrainConfig(
+        name="pi05_aloha_pen_uncap",
+        model=pi0_config.Pi0Config(pi05=True),
+        data=LeRobotAlohaDataConfig(
+            repo_id="physical-intelligence/aloha_pen_uncap_diverse",
+            assets=AssetsConfig(
+                assets_dir="gs://openpi-assets/checkpoints/pi05_base/assets",
+                asset_id="trossen",
+            ),
+            default_prompt="uncap the pen",
+            repack_transforms=_transforms.Group(
+                inputs=[
+                    _transforms.RepackTransform(
+                        {
+                            "images": {
+                                "cam_high": "observation.images.cam_high",
+                                "cam_left_wrist": "observation.images.cam_left_wrist",
+                                "cam_right_wrist": "observation.images.cam_right_wrist",
+                            },
+                            "state": "observation.state",
+                            "actions": "action",
+                        }
+                    )
+                ]
+            ),
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+        num_train_steps=20_000,
+        batch_size=64,
+    ),
+    #
+    # Fine-tuning DROID configs.
+    #
+    TrainConfig(
+        # This config is for fine-tuning pi0-FAST-base on the *full* DROID dataset.
+        # We use RLDS data loading to make training on this large dataset tractable.
+        # For fine-tuning on your own DROID dataset, see below.
+        name="pi0_fast_full_droid_finetune",
+        model=pi0_fast.Pi0FASTConfig(
+            action_dim=8,
+            action_horizon=16,
+            max_token_len=180,
+        ),
+        data=RLDSDroidDataConfig(
+            repo_id="droid",
+            # Set this to the path to your DROID RLDS dataset (the parent directory of the `droid` directory).
+            rlds_data_dir="<path_to_droid_rlds_dataset>",
+            action_space=droid_rlds_dataset.DroidActionSpace.JOINT_POSITION,
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_fast_base/params"),
+        lr_schedule=_optimizer.CosineDecaySchedule(
+            warmup_steps=1_000,
+            peak_lr=5e-5,
+            decay_steps=1_000_000,
+            decay_lr=5e-5,
+        ),
+        num_train_steps=100_000,  # 100k steps should be sufficient, takes ~2 days on 8x H100s
+        batch_size=256,
+        log_interval=100,
+        save_interval=5000,
+        keep_period=20_000,
+        num_workers=0,  # Important: RLDS DataLoader requires num_workers=0, handles multi-processing internally
+    ),
+    TrainConfig(
+        # This config is for fine-tuning pi05 on the *full* DROID dataset.
+        # We use RLDS data loading to make training on this large dataset tractable.
+        # For fine-tuning on your own DROID dataset, see below.
+        name="pi05_full_droid_finetune",
+        model=pi0_config.Pi0Config(
+            pi05=True,
+            action_dim=32,
+            action_horizon=16,
+        ),
+        data=RLDSDroidDataConfig(
+            repo_id="droid",
+            # Set this to the path to your DROID RLDS dataset (the parent directory of the `droid` directory).
+            rlds_data_dir="/mnt/pi-data/kevin",
+            action_space=droid_rlds_dataset.DroidActionSpace.JOINT_POSITION,
+            assets=AssetsConfig(
+                assets_dir="gs://openpi-assets/checkpoints/pi05_base/assets/",
+                asset_id="droid",
+            ),
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+        lr_schedule=_optimizer.CosineDecaySchedule(
+            warmup_steps=1_000,
+            peak_lr=5e-5,
+            decay_steps=1_000_000,
+            decay_lr=5e-5,
+        ),
+        num_train_steps=100_000,
+        batch_size=256,
+        log_interval=100,
+        save_interval=5000,
+        keep_period=10_000,
+        num_workers=0,  # Important: RLDS DataLoader requires num_workers=0, handles multi-processing internally
+    ),
+    TrainConfig(
+        # This config is for fine-tuning pi05-DROID on a custom (smaller) DROID dataset.
+        # Here, we use LeRobot data format (like for all other fine-tuning examples)
+        # To convert your custom DROID dataset (<10s of hours) to LeRobot format, see examples/droid/convert_droid_data_to_lerobot.py
+        name="pi05_droid_finetune",
+        model=pi0_config.Pi0Config(
+            pi05=True,
+            action_dim=32,  # pi05 is trained with 32-dim actions
+            action_horizon=16,
+        ),
+        data=LeRobotDROIDDataConfig(
+            # Replace with your custom DROID LeRobot dataset repo id.
+            repo_id="your_hf_username/my_droid_dataset",
+            base_config=DataConfig(prompt_from_task=True),
+            assets=AssetsConfig(
+                # Important: reuse the original DROID norm stats during fine-tuning!
+                assets_dir="gs://openpi-assets/checkpoints/pi05_droid/assets",
+                asset_id="droid",
+            ),
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_droid/params"),
+        num_train_steps=20_000,
+        batch_size=32,
+    ),
+    #
+    # ALOHA Sim configs. This config is used to demonstrate how to train on a simple simulated environment.
+    #
+    TrainConfig(
+        name="pi0_aloha_sim",
+        model=pi0_config.Pi0Config(),
+        data=LeRobotAlohaDataConfig(
+            repo_id="lerobot/aloha_sim_transfer_cube_human",
+            default_prompt="Transfer cube",
+            use_delta_joint_actions=False,
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"),
+        num_train_steps=20_000,
+    ),
+    #
+    # Debugging configs.
+    #
+    TrainConfig(
+        name="debug",
+        data=FakeDataConfig(),
+        batch_size=2,
+        model=pi0_config.Pi0Config(paligemma_variant="dummy", action_expert_variant="dummy"),
+        save_interval=100,
+        overwrite=True,
+        exp_name="debug",
+        num_train_steps=10,
+        wandb_enabled=False,
+    ),
+    TrainConfig(
+        name="debug_restore",
+        data=FakeDataConfig(),
+        batch_size=2,
+        model=pi0_config.Pi0Config(paligemma_variant="dummy", action_expert_variant="dummy"),
+        weight_loader=weight_loaders.CheckpointWeightLoader("./checkpoints/debug/debug/9/params"),
+        overwrite=True,
+        exp_name="debug",
+        num_train_steps=10,
+        wandb_enabled=False,
+    ),
+    TrainConfig(
+        name="debug_pi05",
+        model=pi0_config.Pi0Config(pi05=True, paligemma_variant="dummy", action_expert_variant="dummy"),
+        data=FakeDataConfig(),
+        batch_size=2,
+        num_train_steps=10,
+        overwrite=True,
+        exp_name="debug_pi05",
+        wandb_enabled=False,
+    ),
+    # RoboArena & PolaRiS configs.
+    *roboarena_config.get_roboarena_configs(),
+    *polaris_config.get_polaris_configs(),
+]
+
+if len({config.name for config in _CONFIGS}) != len(_CONFIGS):
+    raise ValueError("Config names must be unique.")
+_CONFIGS_DICT = {config.name: config for config in _CONFIGS}
+
+
+def cli() -> TrainConfig:
+    return tyro.extras.overridable_config_cli({k: (k, v) for k, v in _CONFIGS_DICT.items()})
+
+
+def get_config(config_name: str) -> TrainConfig:
+    """Get a config by name."""
+    if config_name not in _CONFIGS_DICT:
+        closest = difflib.get_close_matches(config_name, _CONFIGS_DICT.keys(), n=1, cutoff=0.0)
+        closest_str = f" Did you mean '{closest[0]}'? " if closest else ""
+        raise ValueError(f"Config '{config_name}' not found.{closest_str}")
+
+    return _CONFIGS_DICT[config_name]

bundle/code/openpi/src/openpi/training/data_loader.py

@@ -0,0 +1,550 @@
+from collections.abc import Iterator, Sequence
+import logging
+import multiprocessing
+import os
+import typing
+from typing import Literal, Protocol, SupportsIndex, TypeVar
+
+import jax
+import jax.numpy as jnp
+import lerobot.common.datasets.lerobot_dataset as lerobot_dataset
+import numpy as np
+import torch
+
+import openpi.models.model as _model
+import openpi.training.config as _config
+from openpi.training.droid_rlds_dataset import DroidRldsDataset
+from openpi.training.kinova_lerobot_v3_dataset import KinovaLeRobotV3Dataset
+from openpi.training.kinova_lerobot_v3_dataset import REPO_ID as KINOVA_TEDDYBEAR_REPO_ID
+import openpi.transforms as _transforms
+
+T_co = TypeVar("T_co", covariant=True)
+
+
+class Dataset(Protocol[T_co]):
+    """Interface for a dataset with random access."""
+
+    def __getitem__(self, index: SupportsIndex) -> T_co:
+        raise NotImplementedError("Subclasses of Dataset should implement __getitem__.")
+
+    def __len__(self) -> int:
+        raise NotImplementedError("Subclasses of Dataset should implement __len__.")
+
+
+class IterableDataset(Protocol[T_co]):
+    """Interface for an iterable dataset."""
+
+    def __iter__(self) -> Iterator[T_co]:
+        raise NotImplementedError("Subclasses of IterableDataset should implement __iter__.")
+
+    def __len__(self) -> int:
+        raise NotImplementedError("Subclasses of Dataset should implement __len__.")
+
+
+class DataLoader(Protocol[T_co]):
+    """Interface for a data loader."""
+
+    def data_config(self) -> _config.DataConfig:
+        """Get the data config for this data loader."""
+        raise NotImplementedError("Subclasses of DataLoader should implement data_config.")
+
+    def __iter__(self) -> Iterator[T_co]:
+        raise NotImplementedError("Subclasses of DataLoader should implement __iter__.")
+
+
+class TransformedDataset(Dataset[T_co]):
+    def __init__(self, dataset: Dataset, transforms: Sequence[_transforms.DataTransformFn]):
+        self._dataset = dataset
+        self._transform = _transforms.compose(transforms)
+
+    def __getitem__(self, index: SupportsIndex) -> T_co:
+        return self._transform(self._dataset[index])
+
+    def __len__(self) -> int:
+        return len(self._dataset)
+
+
+class IterableTransformedDataset(IterableDataset[T_co]):
+    def __init__(
+        self,
+        dataset: IterableDataset,
+        transforms: Sequence[_transforms.DataTransformFn],
+        *,
+        is_batched: bool = False,
+    ):
+        self._dataset = dataset
+        self._transform = _transforms.compose(transforms)
+        self._is_batched = is_batched
+
+    def __iter__(self):
+        for sample in self._dataset:
+            if self._is_batched:
+                # Transforms are designed to be applied to individual samples. So we need to split the batch into
+                # individual samples and apply the transform to each sample individually.
+                batch_size = next(v.shape[0] for v in sample.values())
+
+                # Split batch into individual samples using tree_map
+                individual_samples = [jax.tree.map(lambda x: x[i], sample) for i in range(batch_size)]  # noqa: B023
+
+                # Transform each sample
+                transformed = [self._transform(s) for s in individual_samples]
+
+                # Recombine batch with tree_map
+                yield jax.tree.map(lambda *x: np.stack(x, axis=0), *transformed)
+            else:
+                yield self._transform(sample)
+
+    def __len__(self) -> int:
+        return len(self._dataset)
+
+
+class FakeDataset(Dataset):
+    def __init__(self, model_config: _model.BaseModelConfig, num_samples: int):
+        self._num_samples = num_samples
+        self._observation_spec, self._action_spec = model_config.inputs_spec()
+
+    def __getitem__(self, index: SupportsIndex) -> dict:
+        rng = jax.random.key(index.__index__())
+
+        def make_from_spec(spec: jax.ShapeDtypeStruct):
+            nonlocal rng
+            rng, data_rng = jax.random.split(rng)
+            # Remove the batch dimension.
+            shape = spec.shape[1:]
+            if spec.dtype == jnp.float32:
+                return jax.random.uniform(data_rng, shape=shape, minval=-1.0, maxval=1.0)
+            if spec.dtype == jnp.int32:
+                return jax.random.randint(data_rng, shape=shape, minval=0, maxval=2048)
+            return jnp.zeros(shape=shape, dtype=spec.dtype)
+
+        observation = jax.tree.map(make_from_spec, self._observation_spec)
+        action = jax.tree.map(make_from_spec, self._action_spec)
+
+        return {
+            **observation.to_dict(),
+            "actions": action,
+        }
+
+    def __len__(self) -> int:
+        return self._num_samples
+
+
+def create_torch_dataset(
+    data_config: _config.DataConfig, action_horizon: int, model_config: _model.BaseModelConfig
+) -> Dataset:
+    """Create a dataset for training."""
+    repo_id = data_config.repo_id
+    if repo_id is None:
+        raise ValueError("Repo ID is not set. Cannot create dataset.")
+    if repo_id == "fake":
+        return FakeDataset(model_config, num_samples=1024)
+    if repo_id == KINOVA_TEDDYBEAR_REPO_ID:
+        return KinovaLeRobotV3Dataset(
+            action_horizon=action_horizon,
+            action_sequence_keys=data_config.action_sequence_keys,
+            load_videos=os.getenv("OPENPI_SKIP_VIDEO_DECODE", "0") != "1",
+        )
+
+    dataset_meta = lerobot_dataset.LeRobotDatasetMetadata(repo_id)
+    dataset = lerobot_dataset.LeRobotDataset(
+        data_config.repo_id,
+        delta_timestamps={
+            key: [t / dataset_meta.fps for t in range(action_horizon)] for key in data_config.action_sequence_keys
+        },
+    )
+
+    if data_config.prompt_from_task:
+        dataset = TransformedDataset(dataset, [_transforms.PromptFromLeRobotTask(dataset_meta.tasks)])
+
+    return dataset
+
+
+def create_rlds_dataset(
+    data_config: _config.DataConfig,
+    action_horizon: int,
+    batch_size: int,
+    *,
+    shuffle: bool = False,
+) -> Dataset:
+    # At the moment, we only support DROID for RLDS datasets.
+    return DroidRldsDataset(
+        data_dir=data_config.rlds_data_dir,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        action_chunk_size=action_horizon,
+        action_space=data_config.action_space,
+        datasets=data_config.datasets,
+    )
+
+
+def transform_dataset(dataset: Dataset, data_config: _config.DataConfig, *, skip_norm_stats: bool = False) -> Dataset:
+    """Transform the dataset by applying the data transforms."""
+    norm_stats = {}
+    if data_config.repo_id != "fake" and not skip_norm_stats:
+        if data_config.norm_stats is None:
+            raise ValueError(
+                "Normalization stats not found. "
+                "Make sure to run `scripts/compute_norm_stats.py --config-name=<your-config>`."
+            )
+        norm_stats = data_config.norm_stats
+
+    return TransformedDataset(
+        dataset,
+        [
+            *data_config.repack_transforms.inputs,
+            *data_config.data_transforms.inputs,
+            _transforms.Normalize(norm_stats, use_quantiles=data_config.use_quantile_norm),
+            *data_config.model_transforms.inputs,
+        ],
+    )
+
+
+def transform_iterable_dataset(
+    dataset: IterableDataset,
+    data_config: _config.DataConfig,
+    *,
+    skip_norm_stats: bool = False,
+    is_batched: bool = False,
+) -> IterableDataset:
+    """Transform the dataset by applying the data transforms."""
+    norm_stats = {}
+    if data_config.repo_id != "fake" and not skip_norm_stats:
+        if data_config.norm_stats is None:
+            raise ValueError(
+                "Normalization stats not found. "
+                "Make sure to run `scripts/compute_norm_stats.py --config-name=<your-config>`."
+            )
+        norm_stats = data_config.norm_stats
+
+    return IterableTransformedDataset(
+        dataset,
+        [
+            *data_config.repack_transforms.inputs,
+            *data_config.data_transforms.inputs,
+            _transforms.Normalize(norm_stats, use_quantiles=data_config.use_quantile_norm),
+            *data_config.model_transforms.inputs,
+        ],
+        is_batched=is_batched,
+    )
+
+
+def create_data_loader(
+    config: _config.TrainConfig,
+    *,
+    sharding: jax.sharding.Sharding | None = None,
+    shuffle: bool = False,
+    num_batches: int | None = None,
+    skip_norm_stats: bool = False,
+    framework: Literal["jax", "pytorch"] = "jax",
+) -> DataLoader[tuple[_model.Observation, _model.Actions]]:
+    """Create a data loader for training.
+
+    Args:
+        config: The training configuration.
+        sharding: The sharding to use for the data loader (JAX only).
+        shuffle: Whether to shuffle the data.
+        num_batches: Determines the number of batches to return.
+        skip_norm_stats: Whether to skip data normalization.
+        framework: The framework to use ("jax" or "pytorch").
+    """
+    data_config = config.data.create(config.assets_dirs, config.model)
+    logging.info(f"data_config: {data_config}")
+
+    if data_config.rlds_data_dir is not None:
+        return create_rlds_data_loader(
+            data_config,
+            action_horizon=config.model.action_horizon,
+            batch_size=config.batch_size,
+            sharding=sharding,
+            shuffle=shuffle,
+            num_batches=num_batches,
+            skip_norm_stats=skip_norm_stats,
+            framework=framework,
+        )
+    return create_torch_data_loader(
+        data_config,
+        model_config=config.model,
+        action_horizon=config.model.action_horizon,
+        batch_size=config.batch_size,
+        sharding=sharding,
+        shuffle=shuffle,
+        num_batches=num_batches,
+        num_workers=config.num_workers,
+        seed=config.seed,
+        skip_norm_stats=skip_norm_stats,
+        framework=framework,
+    )
+
+
+def create_torch_data_loader(
+    data_config: _config.DataConfig,
+    model_config: _model.BaseModelConfig,
+    action_horizon: int,
+    batch_size: int,
+    *,
+    sharding: jax.sharding.Sharding | None = None,
+    skip_norm_stats: bool = False,
+    shuffle: bool = False,
+    num_batches: int | None = None,
+    num_workers: int = 0,
+    seed: int = 0,
+    framework: str = "jax",
+) -> DataLoader[tuple[_model.Observation, _model.Actions]]:
+    """Create a data loader for training.
+
+    Args:
+        data_config: The data configuration.
+        action_horizon: The action horizon.
+        batch_size: The batch size.
+        sharding: The sharding to use for the data loader. If None, the data loader will
+            use a single device sharding.
+        skip_norm_stats: Whether to skip data normalization.
+        shuffle: Whether to shuffle the data.
+        num_batches: Determines the number of batches to return. If the number exceeds the
+            number of batches in the dataset, the data loader will loop over the dataset.
+            If not provided, will iterate over the dataset indefinitely.
+        num_workers: The number of worker processes to use. If zero, the data loader will
+            execute in the main process.
+        seed: The seed to use for shuffling the data.
+    """
+    dataset = create_torch_dataset(data_config, action_horizon, model_config)
+    dataset = transform_dataset(dataset, data_config, skip_norm_stats=skip_norm_stats)
+
+    # Use TorchDataLoader for both frameworks
+    # For PyTorch DDP, create DistributedSampler and divide batch size by world size
+    # For JAX, divide by process count
+    sampler = None
+    if framework == "pytorch":
+        if torch.distributed.is_initialized():
+            sampler = torch.utils.data.distributed.DistributedSampler(
+                dataset,
+                num_replicas=torch.distributed.get_world_size(),
+                rank=torch.distributed.get_rank(),
+                shuffle=shuffle,
+                drop_last=True,
+            )
+            local_batch_size = batch_size // torch.distributed.get_world_size()
+        else:
+            local_batch_size = batch_size
+    else:
+        local_batch_size = batch_size // jax.process_count()
+
+    logging.info(f"local_batch_size: {local_batch_size}")
+    data_loader = TorchDataLoader(
+        dataset,
+        local_batch_size=local_batch_size,
+        sharding=None if framework == "pytorch" else sharding,
+        shuffle=(sampler is None and shuffle),  # Don't shuffle if using sampler
+        sampler=sampler,
+        num_batches=num_batches,
+        num_workers=num_workers,
+        seed=seed,
+        framework=framework,
+    )
+
+    return DataLoaderImpl(data_config, data_loader)
+
+
+def create_rlds_data_loader(
+    data_config: _config.DataConfig,
+    action_horizon: int,
+    batch_size: int,
+    *,
+    sharding: jax.sharding.Sharding | None = None,
+    skip_norm_stats: bool = False,
+    shuffle: bool = False,
+    num_batches: int | None = None,
+    framework: str = "jax",
+) -> DataLoader[tuple[_model.Observation, _model.Actions]]:
+    """Create an RLDS data loader for training.
+
+    Note: This data loader requires some extra dependencies -- see examples/droid/README_train.md
+
+    Args:
+        data_config: The data configuration.
+        action_horizon: The action horizon.
+        batch_size: The batch size.
+        sharding: The sharding to use for the data loader. If None, the data loader will
+            use a single device sharding.
+        skip_norm_stats: Whether to skip data normalization.
+        shuffle: Whether to shuffle the data.
+        num_batches: Determines the number of batches to return. If the number exceeds the
+            number of batches in the dataset, the data loader will loop over the dataset.
+            If not provided, will iterate over the dataset indefinitely.
+    """
+    if framework == "pytorch":
+        raise NotImplementedError("PyTorch RLDS data loader is not supported yet")
+    dataset = create_rlds_dataset(data_config, action_horizon, batch_size, shuffle=shuffle)
+    dataset = transform_iterable_dataset(dataset, data_config, skip_norm_stats=skip_norm_stats, is_batched=True)
+
+    data_loader = RLDSDataLoader(
+        dataset,
+        sharding=sharding,
+        num_batches=num_batches,
+    )
+
+    return DataLoaderImpl(data_config, data_loader)
+
+
+class TorchDataLoader:
+    """Torch data loader implementation."""
+
+    def __init__(
+        self,
+        dataset,
+        local_batch_size: int,
+        *,
+        sharding: jax.sharding.Sharding | None = None,
+        shuffle: bool = False,
+        sampler: torch.utils.data.Sampler | None = None,
+        num_batches: int | None = None,
+        num_workers: int = 0,
+        seed: int = 0,
+        framework: str = "jax",
+    ):
+        """Create a PyTorch data loader.
+
+        Args:
+            dataset: The dataset to load.
+            local_batch_size: The local batch size for each process.
+            sharding: The sharding to use for the data loader.
+            shuffle: Whether to shuffle the data.
+            num_batches: If provided, determines the number of returned batches. If the
+                number is larger than the number of batches in the dataset, the data loader
+                will loop over the dataset. If not provided, will iterate over the dataset
+                indefinitely.
+            num_workers: The number of worker processes to use. If zero, the data loader will
+                execute in the main process.
+            seed: The seed to use for shuffling the data.
+        """
+        if jax.process_count() > 1:
+            raise NotImplementedError("Data loading with multiple processes is not supported.")
+
+        if len(dataset) < local_batch_size:
+            raise ValueError(f"Local batch size ({local_batch_size}) is larger than the dataset size ({len(dataset)}).")
+
+        # Store sharding - None for PyTorch, JAX sharding for JAX
+        self._sharding = sharding
+        if sharding is None and framework == "jax":
+            # Use data parallel sharding by default for JAX only.
+            self._sharding = jax.sharding.NamedSharding(
+                jax.sharding.Mesh(jax.devices(), ("B",)),
+                jax.sharding.PartitionSpec("B"),
+            )
+        self._num_batches = num_batches
+
+        mp_context = None
+        if num_workers > 0:
+            mp_context_name = os.getenv("OPENPI_TORCH_DATALOADER_MP_CONTEXT", "spawn")
+            if mp_context_name != "default":
+                mp_context = multiprocessing.get_context(mp_context_name)
+
+        generator = torch.Generator()
+        generator.manual_seed(seed)
+        self._data_loader = torch.utils.data.DataLoader(
+            typing.cast(torch.utils.data.Dataset, dataset),
+            batch_size=local_batch_size,
+            shuffle=(sampler is None and shuffle),  # Don't shuffle if using sampler
+            sampler=sampler,
+            num_workers=num_workers,
+            multiprocessing_context=mp_context,
+            persistent_workers=num_workers > 0,
+            collate_fn=_collate_fn,
+            worker_init_fn=_worker_init_fn,
+            drop_last=True,
+            generator=generator,
+        )
+
+    @property
+    def torch_loader(self) -> torch.utils.data.DataLoader:
+        return self._data_loader
+
+    def __iter__(self):
+        num_items = 0
+        while True:
+            data_iter = iter(self._data_loader)
+            while True:
+                if self._num_batches is not None and num_items >= self._num_batches:
+                    return
+                try:
+                    batch = next(data_iter)
+                except StopIteration:
+                    break  # We've exhausted the dataset. Create a new iterator and start over.
+                num_items += 1
+                # For JAX, convert to sharded arrays; for PyTorch, return torch tensors
+                if self._sharding is not None:
+                    yield jax.tree.map(lambda x: jax.make_array_from_process_local_data(self._sharding, x), batch)
+                else:
+                    yield jax.tree.map(torch.as_tensor, batch)
+
+
+def _collate_fn(items):
+    """Collate the batch elements into batched numpy arrays."""
+    # Make sure to convert to numpy arrays before stacking since some of the incoming elements
+    # may be JAX arrays.
+    return jax.tree.map(lambda *xs: np.stack([np.asarray(x) for x in xs], axis=0), *items)
+
+
+def _worker_init_fn(worker_id: int) -> None:
+    """Tell JAX inside the worker process not to preallocate the GPU memory."""
+    # NOTE: This is called after jax is imported inside the worker process. This
+    # means that this approach will not work for selecting the backend.
+    os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
+    os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
+
+
+class RLDSDataLoader:
+    """Shallow wrapper around the DROID data loader to make it compatible with openpi.
+
+    All batching already happens in the DROID dataset, so we don't need to do anything here.
+    """
+
+    def __init__(
+        self,
+        dataset: DroidRldsDataset,
+        *,
+        sharding: jax.sharding.Sharding | None = None,
+        num_batches: int | None = None,
+    ):
+        self._dataset = dataset
+        self._num_batches = num_batches
+
+        if jax.process_count() > 1:
+            raise NotImplementedError("Data loading with multiple processes is not supported.")
+
+        if sharding is None:
+            # Use data parallel sharding by default.
+            sharding = jax.sharding.NamedSharding(
+                jax.sharding.Mesh(jax.devices(), ("B",)),
+                jax.sharding.PartitionSpec("B"),
+            )
+
+        self._sharding = sharding
+        self._num_batches = num_batches
+
+    def __iter__(self):
+        num_items = 0
+        while True:
+            data_iter = iter(self._dataset)
+            while True:
+                if self._num_batches is not None and num_items >= self._num_batches:
+                    return
+                try:
+                    batch = next(data_iter)
+                except StopIteration:
+                    break  # We've exhausted the dataset. Create a new iterator and start over.
+                num_items += 1
+                yield jax.tree.map(lambda x: jax.make_array_from_process_local_data(self._sharding, x), batch)
+
+
+class DataLoaderImpl(DataLoader):
+    def __init__(self, data_config: _config.DataConfig, data_loader: TorchDataLoader | RLDSDataLoader):
+        self._data_config = data_config
+        self._data_loader = data_loader
+
+    def data_config(self) -> _config.DataConfig:
+        return self._data_config
+
+    def __iter__(self):
+        for batch in self._data_loader:
+            yield _model.Observation.from_dict(batch), batch["actions"]

bundle/code/openpi/src/openpi/training/kinova_lerobot_v3_dataset.py

@@ -0,0 +1,169 @@
+from collections.abc import Sequence
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import torch
+
+from lerobot.common.datasets import utils as lerobot_utils
+from lerobot.common.datasets.video_utils import decode_video_frames
+from lerobot.common.datasets.video_utils import get_safe_default_codec
+
+REPO_ID = "lsnu/TeddyBearKinovaTestSetLeRobot"
+
+
+def _to_python(value: Any) -> Any:
+    if isinstance(value, np.generic):
+        return value.item()
+    if isinstance(value, np.ndarray):
+        return value.tolist()
+    return value
+
+
+def locate_dataset_root() -> Path:
+    candidates = [
+        Path("/workspace/data/teddybear_kinova_full"),
+        Path.home() / ".cache" / "huggingface" / "lerobot" / REPO_ID,
+    ]
+    for candidate in candidates:
+        if (candidate / "meta" / "info.json").exists():
+            return candidate
+    raise FileNotFoundError(
+        "TeddyBear Kinova dataset not found locally. Expected it under "
+        "/workspace/data/teddybear_kinova_full or ~/.cache/huggingface/lerobot/lsnu/TeddyBearKinovaTestSetLeRobot."
+    )
+
+
+class KinovaLeRobotV3Dataset:
+    def __init__(
+        self,
+        action_horizon: int,
+        action_sequence_keys: Sequence[str],
+        *,
+        tolerance_s: float = 0.05,
+        video_backend: str | None = None,
+        load_videos: bool = True,
+    ):
+        self.root = locate_dataset_root()
+        self.info = json.loads((self.root / "meta" / "info.json").read_text())
+        self.fps = int(self.info["fps"])
+        self.features = self.info["features"]
+        self.video_keys = [key for key, feature in self.features.items() if feature["dtype"] == "video"]
+        self.camera_keys = list(self.video_keys)
+        self.video_shapes = {
+            key: self._feature_shape_to_chw(self.features[key]["shape"]) for key in self.video_keys
+        }
+        # TorchCodec is discoverable in this environment but not linked against a usable FFmpeg runtime.
+        # Prefer the stable CPU pyav path unless an explicit backend is requested.
+        self.video_backend = video_backend or "pyav"
+        self.tolerance_s = tolerance_s
+        self.load_videos = load_videos
+
+        tasks_df = pd.read_parquet(self.root / "meta" / "tasks.parquet")
+        self.tasks = {int(row["task_index"]): str(index) for index, row in tasks_df.iterrows()}
+
+        episodes_df = pd.concat(
+            [pd.read_parquet(path) for path in sorted((self.root / "meta" / "episodes").rglob("*.parquet"))],
+            ignore_index=True,
+        ).sort_values("episode_index")
+        self.episodes = {
+            int(record["episode_index"]): {key: _to_python(value) for key, value in record.items()}
+            for record in episodes_df.to_dict("records")
+        }
+
+        data_df = pd.concat(
+            [pd.read_parquet(path) for path in sorted((self.root / "data").rglob("*.parquet"))],
+            ignore_index=True,
+        ).sort_values("index")
+        self.data = {column: data_df[column].tolist() for column in data_df.columns}
+
+        self.episode_data_index = lerobot_utils.get_episode_data_index(self.episodes)
+
+        delta_timestamps = {key: [t / self.fps for t in range(action_horizon)] for key in action_sequence_keys}
+        lerobot_utils.check_delta_timestamps(delta_timestamps, self.fps, self.tolerance_s)
+        self.delta_indices = lerobot_utils.get_delta_indices(delta_timestamps, self.fps)
+
+    def __len__(self) -> int:
+        return len(self.data["index"])
+
+    @staticmethod
+    def _feature_shape_to_chw(shape: Sequence[int]) -> tuple[int, int, int]:
+        if len(shape) != 3:
+            raise ValueError(f"Expected a 3D video shape, got {shape}")
+        if shape[-1] == 3:
+            height, width, channels = shape
+            return channels, height, width
+        channels, height, width = shape
+        return channels, height, width
+
+    def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int]], dict[str, torch.BoolTensor]]:
+        ep_start = self.episode_data_index["from"][ep_idx].item()
+        ep_end = self.episode_data_index["to"][ep_idx].item()
+        query_indices = {
+            key: [max(ep_start, min(ep_end - 1, idx + delta)) for delta in delta_idx]
+            for key, delta_idx in self.delta_indices.items()
+        }
+        padding = {
+            f"{key}_is_pad": torch.BoolTensor([(idx + delta < ep_start) | (idx + delta >= ep_end) for delta in delta_idx])
+            for key, delta_idx in self.delta_indices.items()
+        }
+        return query_indices, padding
+
+    def _get_video_path(self, ep_idx: int, vid_key: str) -> Path:
+        episode = self.episodes[ep_idx]
+        return self.root / self.info["video_path"].format(
+            video_key=vid_key,
+            chunk_index=int(episode[f"videos/{vid_key}/chunk_index"]),
+            file_index=int(episode[f"videos/{vid_key}/file_index"]),
+        )
+
+    def _query_videos(self, timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
+        if not self.load_videos:
+            return {
+                vid_key: torch.zeros((len(query_ts), *self.video_shapes[vid_key]), dtype=torch.float32).squeeze(0)
+                for vid_key, query_ts in timestamps.items()
+            }
+        episode = self.episodes[ep_idx]
+        item = {}
+        for vid_key, query_ts in timestamps.items():
+            offset = float(episode[f"videos/{vid_key}/from_timestamp"])
+            frames = decode_video_frames(
+                self._get_video_path(ep_idx, vid_key),
+                [ts + offset for ts in query_ts],
+                self.tolerance_s,
+                self.video_backend,
+            )
+            item[vid_key] = frames.squeeze(0)
+        return item
+
+    def __getitem__(self, idx: int) -> dict[str, Any]:
+        item = {key: self.data[key][idx] for key in self.data}
+        ep_idx = int(item["episode_index"])
+
+        query_indices, padding = self._get_query_indices(idx, ep_idx)
+        item.update(padding)
+        for key, indices in query_indices.items():
+            if key in self.video_keys:
+                continue
+            item[key] = torch.stack([torch.tensor(self.data[key][query_idx]) for query_idx in indices])
+
+        # LeRobot v3 keeps the raw sync-clock timestamp for tabular data, but the episode videos are
+        # encoded on the nominal frame grid implied by `frame_index` and `fps`. Querying by the raw
+        # timestamp causes systematic tail mismatches at episode ends, so use frame-index time for
+        # video lookup.
+        current_video_ts = float(item["frame_index"]) / self.fps
+        query_timestamps = {key: [current_video_ts] for key in self.video_keys}
+        item.update(self._query_videos(query_timestamps, ep_idx))
+
+        for key, value in list(item.items()):
+            if isinstance(value, torch.Tensor):
+                continue
+            if isinstance(value, str):
+                continue
+            item[key] = torch.tensor(value)
+
+        item["task"] = self.tasks[int(item["task_index"].item())]
+        return item

bundle/run_rollout_preview.sh

@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+OPENPI_ROOT="${1:-$HOME/workspace/teddybear_openpi/openpi}"
+CHECKPOINT_DIR="${2:-$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/4000}"
+OUT_ROOT="${3:-$HOME/workspace/teddybear_rollout}"
+BUNDLE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+mkdir -p "$OUT_ROOT"
+cd "$OPENPI_ROOT"
+
+export JAX_PLATFORMS="${JAX_PLATFORMS:-cuda}"
+
+.venv/bin/python "$BUNDLE_ROOT/sim/rollout_pi_policy.py" \
+  --checkpoint-dir "$CHECKPOINT_DIR" \
+  --output-json "$OUT_ROOT/pi_rollout.json"
+
+python "$BUNDLE_ROOT/sim/render_pose_sequence.py" \
+  --poses-json "$OUT_ROOT/pi_rollout.json" \
+  --output "$OUT_ROOT/pi_rollout.gif"
+
+echo "$OUT_ROOT"

bundle/setup_same_a100.sh

@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+BUNDLE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+TARGET_ROOT="${1:-$HOME/workspace/teddybear_openpi}"
+
+mkdir -p "$TARGET_ROOT"
+mkdir -p "$TARGET_ROOT/openpi"
+mkdir -p "$TARGET_ROOT/sim"
+
+if ! command -v uv >/dev/null 2>&1; then
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+  export PATH="$HOME/.cargo/bin:$PATH"
+fi
+
+if ! command -v hf >/dev/null 2>&1; then
+  curl -LsSf https://hf.co/cli/install.sh | bash -s
+fi
+
+if [[ -n "${HF_TOKEN:-}" ]]; then
+  hf auth whoami >/dev/null 2>&1 || hf auth login --token "$HF_TOKEN"
+fi
+
+cp -a "$BUNDLE_ROOT/code/openpi/." "$TARGET_ROOT/openpi/"
+cp -a "$BUNDLE_ROOT/sim/." "$TARGET_ROOT/sim/"
+
+cd "$TARGET_ROOT/openpi"
+uv sync --frozen
+. .venv/bin/activate
+pip install scipy opencv-python-headless
+
+echo "$TARGET_ROOT"

bundle/sim/build_scene.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pandas as pd
+from PIL import Image
+
+from scene_tools import (
+    create_background_inpaint,
+    load_scene_calibration,
+    render_scene,
+    save_scene_json,
+)
+
+
+def _save_mask_overlay(rgb: np.ndarray, mask: np.ndarray, color: tuple[int, int, int], path: Path) -> None:
+    overlay = rgb.copy()
+    overlay[mask] = color
+    Image.fromarray(overlay).save(path)
+
+
+def _save_box_outline(rgb: np.ndarray, polygon_world: np.ndarray, calibration, path: Path) -> None:
+    canvas = rgb.copy()
+    image_points, _ = cv2.projectPoints(
+        polygon_world.astype(np.float64), calibration.rvec, calibration.tvec, calibration.camera_matrix, None
+    )
+    pts = np.round(image_points[:, 0, :]).astype(np.int32)
+    cv2.polylines(canvas, [pts], True, (255, 0, 0), 4)
+    Image.fromarray(canvas).save(path)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--session-root",
+        default="/workspace/data/teddybear_raw/session_20260327_165944_bear",
+        help="Raw session root used for calibration.",
+    )
+    parser.add_argument(
+        "--sync-row-index",
+        type=int,
+        default=0,
+        help="Row of sync_index.csv to use as the start frame.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default="/workspace/kinova_scene_sim/outputs",
+        help="Directory for the scene JSON and visual checks.",
+    )
+    args = parser.parse_args()
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    calibration = load_scene_calibration(args.session_root, args.sync_row_index)
+    sync = pd.read_csv(Path(args.session_root) / "sync_index.csv")
+    start_q = sync.iloc[args.sync_row_index][[f"q{i}_deg" for i in range(1, 8)]].to_numpy(np.float64)
+    motion_q = sync.iloc[[0, 56, 70, 84, 98, 112]][[f"q{i}_deg" for i in range(1, 8)]].to_numpy(np.float64)
+
+    background = create_background_inpaint(calibration.rgb)
+    scene_json = output_dir / f"{Path(args.session_root).name}_scene_fit.json"
+    save_scene_json(calibration, scene_json)
+
+    Image.fromarray(calibration.rgb).save(output_dir / "real_start.png")
+    Image.fromarray(background).save(output_dir / "background_inpaint.png")
+
+    _save_mask_overlay(calibration.rgb, calibration.box_mask, (255, 0, 0), output_dir / "box_mask_overlay.png")
+    _save_mask_overlay(calibration.rgb, calibration.teddy_mask, (0, 255, 0), output_dir / "teddy_mask_overlay.png")
+    _save_box_outline(calibration.rgb, calibration.box_world_polygon, calibration, output_dir / "box_world_outline.png")
+
+    start_render = render_scene(calibration, start_q, background=background)
+    Image.fromarray(start_render).save(output_dir / "scene_start_render.png")
+
+    colors = [
+        (255, 64, 64),
+        (64, 255, 64),
+        (64, 64, 255),
+        (255, 255, 64),
+        (255, 64, 255),
+        (64, 255, 255),
+    ]
+    motion_render = background.copy()
+    for q_deg, color in zip(motion_q, colors):
+        motion_render = render_scene(calibration, q_deg, background=motion_render, color=color, alpha=0.95)
+    Image.fromarray(motion_render).save(output_dir / "scene_motion_fit.png")
+
+    side_by_side = np.concatenate([calibration.rgb, start_render], axis=1)
+    Image.fromarray(side_by_side).save(output_dir / "real_vs_scene_start.png")
+
+    print(f"saved scene json to {scene_json}")
+    print(f"start frame: azure_rgb/{calibration.azure_rgb_seq:06d}.jpg")
+    print(f"depth frame: azure_depth/{calibration.azure_depth_seq:06d}.png")
+    print(f"robot seq: {calibration.robot_seq}")
+    print(f"box height estimate: {calibration.box_height_m:.3f} m")
+    print(f"teddy height estimate: {calibration.teddy_height_m:.3f} m")
+    print(f"teddy center world: {calibration.teddy_world_center.tolist()}")
+
+
+if __name__ == "__main__":
+    main()

bundle/sim/export_pi_actions.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from PIL import Image
+
+
+def main() -> None:
+    t0 = time.time()
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint-dir", required=True, help="Local checkpoint directory, e.g. .../4000")
+    parser.add_argument("--session-root", default="/workspace/data/teddybear_raw/session_20260327_165944_bear")
+    parser.add_argument("--sync-row-index", type=int, default=0)
+    parser.add_argument("--config-name", default="pi05_kinova_teddybear")
+    parser.add_argument("--output-json", default="/workspace/kinova_scene_sim/outputs/pi_actions.json")
+    parser.add_argument("--prompt", default="pick up the teddy bear and place it in the red box")
+    args = parser.parse_args()
+
+    session_root = Path(args.session_root)
+    print(f"[export] loading session from {session_root}", flush=True)
+    sync = pd.read_csv(session_root / "sync_index.csv")
+    row = sync.iloc[args.sync_row_index]
+
+    azure_rgb = np.array(Image.open(session_root / row["azure_rgb_file"]).convert("RGB"))
+    wrist_rgb = np.array(Image.open(session_root / row["rgb_file"]).convert("RGB"))
+    state = np.array(
+        [
+            row["tool_x_m"],
+            row["tool_y_m"],
+            row["tool_z_m"],
+            row["tool_theta_x_deg"],
+            row["tool_theta_y_deg"],
+            row["tool_theta_z_deg"],
+            row["gripper_pos"],
+        ],
+        dtype=np.float32,
+    )
+    print(f"[export] loaded observation in {time.time() - t0:.2f}s", flush=True)
+
+    print("[export] importing openpi modules", flush=True)
+    from openpi.policies import policy_config as _policy_config
+    from openpi.training import config as _config
+
+    print(f"[export] imported openpi in {time.time() - t0:.2f}s", flush=True)
+    print(f"[export] building train config {args.config_name}", flush=True)
+    train_config = _config.get_config(args.config_name)
+    print(f"[export] creating policy from {args.checkpoint_dir}", flush=True)
+    policy = _policy_config.create_trained_policy(train_config, Path(args.checkpoint_dir))
+    print(f"[export] policy ready in {time.time() - t0:.2f}s", flush=True)
+    print("[export] running inference", flush=True)
+    results = policy.infer(
+        {
+            "observation/state": state,
+            "observation/image": azure_rgb,
+            "observation/wrist_image": wrist_rgb,
+            "prompt": args.prompt,
+        }
+    )
+    print(f"[export] inference done in {time.time() - t0:.2f}s", flush=True)
+
+    actions = np.asarray(results["actions"], dtype=np.float32)
+    output = {
+        "checkpoint_dir": str(Path(args.checkpoint_dir)),
+        "session_root": str(session_root),
+        "sync_row_index": int(args.sync_row_index),
+        "azure_rgb_file": str(row["azure_rgb_file"]),
+        "wrist_rgb_file": str(row["rgb_file"]),
+        "state": state.tolist(),
+        "actions": actions.tolist(),
+    }
+
+    output_path = Path(args.output_json)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(json.dumps(output, indent=2))
+    print(f"saved action chunk to {output_path}")
+
+
+if __name__ == "__main__":
+    main()

bundle/sim/render_pose_sequence.py

@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pandas as pd
+from PIL import Image
+from scipy.optimize import least_squares
+
+from scene_tools import create_background_inpaint, kinova_fk_points_and_tool_pose, load_scene_calibration, render_scene
+
+
+def euler_xyz_deg_to_matrix(theta_x_deg: float, theta_y_deg: float, theta_z_deg: float) -> np.ndarray:
+    rx, ry, rz = np.deg2rad([theta_x_deg, theta_y_deg, theta_z_deg])
+    cx, sx = np.cos(rx), np.sin(rx)
+    cy, sy = np.cos(ry), np.sin(ry)
+    cz, sz = np.cos(rz), np.sin(rz)
+    rot_x = np.array([[1, 0, 0], [0, cx, -sx], [0, sx, cx]], dtype=np.float64)
+    rot_y = np.array([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]], dtype=np.float64)
+    rot_z = np.array([[cz, -sz, 0], [sz, cz, 0], [0, 0, 1]], dtype=np.float64)
+    return rot_z @ rot_y @ rot_x
+
+
+def solve_ik(target_pose: np.ndarray, q_init_deg: np.ndarray) -> np.ndarray:
+    target_pos = target_pose[:3].astype(np.float64)
+    target_rot = euler_xyz_deg_to_matrix(*target_pose[3:6])
+    q_init = np.asarray(q_init_deg, dtype=np.float64)
+
+    def residual(q_deg: np.ndarray) -> np.ndarray:
+        _, tool = kinova_fk_points_and_tool_pose(q_deg)
+        pos_err = tool[:3, 3] - target_pos
+        rot_err_mat = tool[:3, :3].T @ target_rot
+        rot_err, _ = cv2.Rodrigues(rot_err_mat)
+        regularize = 0.01 * (q_deg - q_init)
+        return np.concatenate([12.0 * pos_err, 0.75 * rot_err.reshape(-1), regularize])
+
+    result = least_squares(residual, q_init, method="lm", max_nfev=120)
+    return result.x.astype(np.float64)
+
+
+def load_target_poses(args: argparse.Namespace) -> tuple[np.ndarray, np.ndarray]:
+    if args.poses_json:
+        payload = json.loads(Path(args.poses_json).read_text())
+        if isinstance(payload, dict) and "actions" in payload:
+            poses = np.asarray(payload["actions"], dtype=np.float64)
+        else:
+            poses = np.asarray(payload, dtype=np.float64)
+        start_q = np.asarray(args.q_init_deg, dtype=np.float64)
+        return poses, start_q
+
+    sync = pd.read_csv(Path(args.session_root) / "sync_index.csv")
+    rows = sync.iloc[args.sync_start : args.sync_stop : args.sync_step]
+    poses = rows[
+        ["tool_x_m", "tool_y_m", "tool_z_m", "tool_theta_x_deg", "tool_theta_y_deg", "tool_theta_z_deg", "gripper_pos"]
+    ].to_numpy(np.float64)
+    start_q = rows.iloc[0][[f"q{i}_deg" for i in range(1, 8)]].to_numpy(np.float64)
+    return poses, start_q
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--session-root", default="/workspace/data/teddybear_raw/session_20260327_165944_bear")
+    parser.add_argument("--output", default="/workspace/kinova_scene_sim/outputs/rollout.gif")
+    parser.add_argument("--poses-json", default=None, help="JSON file containing a list of 7D tool poses.")
+    parser.add_argument(
+        "--q-init-deg",
+        nargs=7,
+        type=float,
+        default=[0.0008167303, 340.0004272461, 180.0014038086, 213.9954223633, 359.9988708496, 309.9989318848, 90.0014266968],
+        help="Initial joint seed for IK when using --poses-json.",
+    )
+    parser.add_argument("--sync-start", type=int, default=0)
+    parser.add_argument("--sync-stop", type=int, default=130)
+    parser.add_argument("--sync-step", type=int, default=8)
+    parser.add_argument("--fps", type=int, default=5)
+    args = parser.parse_args()
+
+    calibration = load_scene_calibration(args.session_root, sync_row_index=0)
+    background = create_background_inpaint(calibration.rgb)
+    poses, q_seed = load_target_poses(args)
+
+    frames: list[Image.Image] = []
+    q_current = q_seed.copy()
+    for idx, pose in enumerate(poses):
+        q_current = solve_ik(pose, q_current)
+        frame = render_scene(calibration, q_current, background=background)
+        # Mark the target tool center on the rendered frame.
+        points_world, _ = kinova_fk_points_and_tool_pose(q_current)
+        _ = points_world  # quiet lint-like readers; pose solving already uses the FK.
+        frames.append(Image.fromarray(frame))
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    frames[0].save(
+        output,
+        save_all=True,
+        append_images=frames[1:],
+        loop=0,
+        duration=int(1000 / max(args.fps, 1)),
+    )
+    print(f"saved rollout gif to {output}")
+
+
+if __name__ == "__main__":
+    main()

bundle/sim/rollout_pi_policy.py

@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from PIL import Image
+
+
+def main() -> None:
+    t0 = time.time()
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint-dir", required=True)
+    parser.add_argument("--session-root", default="/workspace/data/teddybear_raw/session_20260327_165944_bear")
+    parser.add_argument("--sync-row-index", type=int, default=0)
+    parser.add_argument("--config-name", default="pi05_kinova_teddybear")
+    parser.add_argument("--prompt", default="pick up the teddy bear and place it in the red box")
+    parser.add_argument("--rollout-steps", type=int, default=24)
+    parser.add_argument("--output-json", default="/workspace/kinova_scene_sim/outputs/pi_rollout.json")
+    args = parser.parse_args()
+
+    session_root = Path(args.session_root)
+    print(f"[rollout] loading session from {session_root}", flush=True)
+    sync = pd.read_csv(session_root / "sync_index.csv")
+    row = sync.iloc[args.sync_row_index]
+
+    azure_rgb = np.array(Image.open(session_root / row["azure_rgb_file"]).convert("RGB"))
+    wrist_rgb = np.array(Image.open(session_root / row["rgb_file"]).convert("RGB"))
+    state = np.array(
+        [
+            row["tool_x_m"],
+            row["tool_y_m"],
+            row["tool_z_m"],
+            row["tool_theta_x_deg"],
+            row["tool_theta_y_deg"],
+            row["tool_theta_z_deg"],
+            row["gripper_pos"],
+        ],
+        dtype=np.float32,
+    )
+    print(f"[rollout] loaded observation in {time.time() - t0:.2f}s", flush=True)
+
+    print("[rollout] importing openpi modules", flush=True)
+    from openpi.policies import policy_config as _policy_config
+    from openpi.training import config as _config
+
+    print(f"[rollout] imported openpi in {time.time() - t0:.2f}s", flush=True)
+    train_config = _config.get_config(args.config_name)
+    print(f"[rollout] creating policy from {args.checkpoint_dir}", flush=True)
+    policy = _policy_config.create_trained_policy(train_config, Path(args.checkpoint_dir))
+    print(f"[rollout] policy ready in {time.time() - t0:.2f}s", flush=True)
+
+    states = [state.astype(np.float32)]
+    chunks = []
+    current_state = state.astype(np.float32)
+    for step_idx in range(args.rollout_steps):
+        results = policy.infer(
+            {
+                "observation/state": current_state,
+                "observation/image": azure_rgb,
+                "observation/wrist_image": wrist_rgb,
+                "prompt": args.prompt,
+            }
+        )
+        chunk = np.asarray(results["actions"], dtype=np.float32)
+        chunks.append(chunk)
+        current_state = chunk[0].copy()
+        states.append(current_state)
+        print(
+            f"[rollout] step={step_idx + 1}/{args.rollout_steps} "
+            f"xyz={current_state[:3].tolist()} grip={float(current_state[6]):.3f}",
+            flush=True,
+        )
+
+    output = {
+        "checkpoint_dir": str(Path(args.checkpoint_dir)),
+        "session_root": str(session_root),
+        "sync_row_index": int(args.sync_row_index),
+        "azure_rgb_file": str(row["azure_rgb_file"]),
+        "wrist_rgb_file": str(row["rgb_file"]),
+        "initial_state": state.tolist(),
+        "states": np.stack(states, axis=0).tolist(),
+        "actions": np.stack(states[1:], axis=0).tolist(),
+        "chunks": np.stack(chunks, axis=0).tolist(),
+    }
+
+    output_path = Path(args.output_json)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(json.dumps(output, indent=2))
+    print(f"[rollout] saved rollout to {output_path}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

bundle/sim/run_policy_preview.py

@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+import argparse
+import os
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+
+def checkpoint_ready(checkpoint_dir: Path) -> bool:
+    return (checkpoint_dir / "_CHECKPOINT_METADATA").is_file() and (checkpoint_dir / "params" / "_METADATA").is_file()
+
+
+def wait_for_checkpoint(checkpoint_dir: Path, poll_s: float) -> None:
+    while not checkpoint_ready(checkpoint_dir):
+        print(f"waiting for checkpoint: {checkpoint_dir}", flush=True)
+        time.sleep(poll_s)
+
+
+def pid_running(pid: int) -> bool:
+    return Path(f"/proc/{pid}").exists()
+
+
+def wait_for_pid_exit(pid: int, poll_s: float) -> None:
+    while pid_running(pid):
+        print(f"waiting for pid to exit: {pid}", flush=True)
+        time.sleep(poll_s)
+
+
+def stage_checkpoint(checkpoint_dir: Path, stage_root: Path) -> Path:
+    stage_root.mkdir(parents=True, exist_ok=True)
+    staged_dir = stage_root / checkpoint_dir.name
+    if staged_dir.exists():
+        shutil.rmtree(staged_dir)
+    subprocess.run(["cp", "-al", str(checkpoint_dir), str(stage_root)], check=True)
+    return staged_dir
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint-dir", default=None, help="Checkpoint step directory, e.g. .../4000")
+    parser.add_argument(
+        "--checkpoint-root",
+        default="/workspace/openpi/checkpoints/pi05_kinova_teddybear/teddybear_kinova_ft_v4_b24_w12_fork",
+        help="Checkpoint experiment root used with --step.",
+    )
+    parser.add_argument("--step", type=int, default=None, help="Checkpoint step under --checkpoint-root.")
+    parser.add_argument("--wait", action="store_true", help="Wait until the checkpoint has finalized before exporting.")
+    parser.add_argument("--poll-s", type=float, default=15.0)
+    parser.add_argument("--wait-for-pid-exit", type=int, default=None, help="Wait for this PID to exit before export.")
+    parser.add_argument(
+        "--stage-root",
+        default=None,
+        help="Optional directory where the finalized checkpoint is hardlink-staged before export.",
+    )
+    parser.add_argument("--session-root", default="/workspace/data/teddybear_raw/session_20260327_165944_bear")
+    parser.add_argument("--sync-row-index", type=int, default=0)
+    parser.add_argument("--prompt", default="pick up the teddy bear and place it in the red box")
+    parser.add_argument(
+        "--python",
+        default="/workspace/openpi/.venv/bin/python",
+        help="Python interpreter used to run the OpenPI export step.",
+    )
+    parser.add_argument(
+        "--jax-platforms",
+        default="cpu",
+        help="Value for JAX_PLATFORMS during action export. Use 'cuda' once training has stopped.",
+    )
+    parser.add_argument(
+        "--output-json",
+        default="/workspace/kinova_scene_sim/outputs/pi_actions_preview.json",
+    )
+    parser.add_argument(
+        "--output-gif",
+        default="/workspace/kinova_scene_sim/outputs/pi_policy_preview.gif",
+    )
+    parser.add_argument("--fps", type=int, default=4)
+    args = parser.parse_args()
+
+    if args.checkpoint_dir is None:
+        if args.step is None:
+            raise ValueError("provide either --checkpoint-dir or --step")
+        checkpoint_dir = Path(args.checkpoint_root) / str(args.step)
+    else:
+        checkpoint_dir = Path(args.checkpoint_dir)
+
+    if args.wait:
+        wait_for_checkpoint(checkpoint_dir, args.poll_s)
+    elif not checkpoint_ready(checkpoint_dir):
+        raise FileNotFoundError(f"checkpoint not ready: {checkpoint_dir}")
+
+    if args.stage_root is not None:
+        checkpoint_dir = stage_checkpoint(checkpoint_dir, Path(args.stage_root))
+        print(f"staged checkpoint to {checkpoint_dir}", flush=True)
+
+    if args.wait_for_pid_exit is not None:
+        wait_for_pid_exit(args.wait_for_pid_exit, args.poll_s)
+
+    export_cmd = [
+        args.python,
+        "/workspace/kinova_scene_sim/export_pi_actions.py",
+        "--checkpoint-dir",
+        str(checkpoint_dir),
+        "--session-root",
+        args.session_root,
+        "--sync-row-index",
+        str(args.sync_row_index),
+        "--prompt",
+        args.prompt,
+        "--output-json",
+        args.output_json,
+    ]
+    export_env = os.environ.copy()
+    export_env["JAX_PLATFORMS"] = args.jax_platforms
+    subprocess.run(export_cmd, check=True, env=export_env)
+
+    render_cmd = [
+        sys.executable,
+        "/workspace/kinova_scene_sim/render_pose_sequence.py",
+        "--session-root",
+        args.session_root,
+        "--poses-json",
+        args.output_json,
+        "--output",
+        args.output_gif,
+        "--fps",
+        str(args.fps),
+    ]
+    subprocess.run(render_cmd, check=True)
+    print(f"saved preview gif to {args.output_gif}")
+
+
+if __name__ == "__main__":
+    main()

bundle/sim/run_policy_rollout_preview.py

@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+import argparse
+import os
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+
+def checkpoint_ready(checkpoint_dir: Path) -> bool:
+    return (checkpoint_dir / "_CHECKPOINT_METADATA").is_file() and (checkpoint_dir / "params" / "_METADATA").is_file()
+
+
+def wait_for_checkpoint(checkpoint_dir: Path, poll_s: float) -> None:
+    while not checkpoint_ready(checkpoint_dir):
+        print(f"waiting for checkpoint: {checkpoint_dir}", flush=True)
+        time.sleep(poll_s)
+
+
+def pid_running(pid: int) -> bool:
+    return Path(f"/proc/{pid}").exists()
+
+
+def wait_for_pid_exit(pid: int, poll_s: float) -> None:
+    while pid_running(pid):
+        print(f"waiting for pid to exit: {pid}", flush=True)
+        time.sleep(poll_s)
+
+
+def stage_checkpoint(checkpoint_dir: Path, stage_root: Path) -> Path:
+    stage_root.mkdir(parents=True, exist_ok=True)
+    staged_dir = stage_root / checkpoint_dir.name
+    if staged_dir.exists():
+        shutil.rmtree(staged_dir)
+    subprocess.run(["cp", "-al", str(checkpoint_dir), str(stage_root)], check=True)
+    return staged_dir
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint-dir", default=None)
+    parser.add_argument(
+        "--checkpoint-root",
+        default="/workspace/openpi/checkpoints/pi05_kinova_teddybear/teddybear_kinova_ft_v4_b24_w12_fork",
+    )
+    parser.add_argument("--step", type=int, default=None)
+    parser.add_argument("--wait", action="store_true")
+    parser.add_argument("--poll-s", type=float, default=15.0)
+    parser.add_argument("--wait-for-pid-exit", type=int, default=None)
+    parser.add_argument("--stage-root", default=None)
+    parser.add_argument("--session-root", default="/workspace/data/teddybear_raw/session_20260327_165944_bear")
+    parser.add_argument("--sync-row-index", type=int, default=0)
+    parser.add_argument("--prompt", default="pick up the teddy bear and place it in the red box")
+    parser.add_argument("--rollout-steps", type=int, default=24)
+    parser.add_argument("--python", default="/workspace/openpi/.venv/bin/python")
+    parser.add_argument("--jax-platforms", default="cpu")
+    parser.add_argument("--output-json", default="/workspace/kinova_scene_sim/outputs/pi_rollout_preview.json")
+    parser.add_argument("--output-gif", default="/workspace/kinova_scene_sim/outputs/pi_rollout_preview.gif")
+    parser.add_argument("--fps", type=int, default=4)
+    args = parser.parse_args()
+
+    if args.checkpoint_dir is None:
+        if args.step is None:
+            raise ValueError("provide either --checkpoint-dir or --step")
+        checkpoint_dir = Path(args.checkpoint_root) / str(args.step)
+    else:
+        checkpoint_dir = Path(args.checkpoint_dir)
+
+    if args.wait:
+        wait_for_checkpoint(checkpoint_dir, args.poll_s)
+    elif not checkpoint_ready(checkpoint_dir):
+        raise FileNotFoundError(f"checkpoint not ready: {checkpoint_dir}")
+
+    if args.stage_root is not None:
+        checkpoint_dir = stage_checkpoint(checkpoint_dir, Path(args.stage_root))
+        print(f"staged checkpoint to {checkpoint_dir}", flush=True)
+
+    if args.wait_for_pid_exit is not None:
+        wait_for_pid_exit(args.wait_for_pid_exit, args.poll_s)
+
+    rollout_cmd = [
+        args.python,
+        "/workspace/kinova_scene_sim/rollout_pi_policy.py",
+        "--checkpoint-dir",
+        str(checkpoint_dir),
+        "--session-root",
+        args.session_root,
+        "--sync-row-index",
+        str(args.sync_row_index),
+        "--prompt",
+        args.prompt,
+        "--rollout-steps",
+        str(args.rollout_steps),
+        "--output-json",
+        args.output_json,
+    ]
+    rollout_env = os.environ.copy()
+    rollout_env["JAX_PLATFORMS"] = args.jax_platforms
+    subprocess.run(rollout_cmd, check=True, env=rollout_env)
+
+    render_cmd = [
+        sys.executable,
+        "/workspace/kinova_scene_sim/render_pose_sequence.py",
+        "--session-root",
+        args.session_root,
+        "--poses-json",
+        args.output_json,
+        "--output",
+        args.output_gif,
+        "--fps",
+        str(args.fps),
+    ]
+    subprocess.run(render_cmd, check=True)
+    print(f"saved rollout preview gif to {args.output_gif}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

bundle/sim/scene_tools.py

@@ -0,0 +1,392 @@
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import cv2
+import numpy as np
+import pandas as pd
+from PIL import Image
+
+
+# Manually fitted table corners for the synced overhead frame used for episode 0.
+# Order: top-left, top-right, bottom-right, bottom-left.
+TABLE_CORNERS_PX = np.array(
+    [
+        [192.0, 138.0],
+        [1635.0, 46.0],
+        [1712.0, 787.0],
+        [185.0, 858.0],
+    ],
+    dtype=np.float64,
+)
+
+# Assumed tabletop size for the large brown work surface in the overhead image.
+TABLE_SIZE_M = (1.52, 0.76)
+
+# Tuned against the pre-grasp motion silhouette.
+CAMERA_FOCAL_PX = 650.0
+ROBOT_BASE_WORLD = np.array([0.50, 0.45, -0.45], dtype=np.float64)
+ROBOT_BASE_YAW_RAD = -2.0
+
+# Kinova tool-center offset relative to the interface frame from FK.
+TOOL_OFFSET_M = 0.12
+
+
+@dataclass(frozen=True)
+class SceneCalibration:
+    session_root: Path
+    sync_row_index: int
+    azure_rgb_seq: int
+    azure_depth_seq: int
+    robot_seq: int
+    rgb_path: Path
+    depth_path: Path
+    rgb: np.ndarray
+    depth: np.ndarray
+    sync_row: dict[str, Any]
+    camera_matrix: np.ndarray
+    rvec: np.ndarray
+    tvec: np.ndarray
+    table_corners_px: np.ndarray
+    table_size_m: tuple[float, float]
+    robot_base_world: np.ndarray
+    robot_base_yaw_rad: float
+    box_mask: np.ndarray
+    teddy_mask: np.ndarray
+    box_world_polygon: np.ndarray
+    teddy_world_center: np.ndarray
+    teddy_world_extent: np.ndarray
+    table_depth_mm: float
+    box_height_m: float
+    teddy_height_m: float
+
+
+def _to_rgb(path: Path) -> np.ndarray:
+    return np.array(Image.open(path).convert("RGB"))
+
+
+def _to_depth(path: Path) -> np.ndarray:
+    return np.array(Image.open(path))
+
+
+def load_sync_dataframe(session_root: Path) -> pd.DataFrame:
+    return pd.read_csv(session_root / "sync_index.csv")
+
+
+def load_scene_calibration(session_root: str | Path, sync_row_index: int = 0) -> SceneCalibration:
+    session_root = Path(session_root)
+    sync = load_sync_dataframe(session_root)
+    row = sync.iloc[sync_row_index]
+
+    azure_rgb_seq = int(row["azure_rgb_seq"])
+    azure_depth_seq = int(row["azure_depth_seq"])
+    robot_seq = int(row["robot_seq"])
+
+    rgb_path = session_root / row["azure_rgb_file"]
+    depth_path = session_root / row["azure_depth_file"]
+    rgb = _to_rgb(rgb_path)
+    depth = _to_depth(depth_path)
+
+    camera_matrix, rvec, tvec = solve_table_camera(TABLE_CORNERS_PX, TABLE_SIZE_M, CAMERA_FOCAL_PX)
+    box_mask = detect_red_box(rgb)
+    teddy_mask = detect_teddy(rgb)
+    box_world_polygon = image_mask_to_world_polygon(box_mask, camera_matrix, rvec, tvec)
+    teddy_world_center, teddy_world_extent = mask_centroid_and_extent_world(teddy_mask, camera_matrix, rvec, tvec)
+
+    table_depth_mm = estimate_table_depth_mm(depth)
+    box_height_m = estimate_height_from_depth(rgb, depth, box_mask, table_depth_mm)
+    teddy_height_m = estimate_height_from_depth(rgb, depth, teddy_mask, table_depth_mm)
+
+    return SceneCalibration(
+        session_root=session_root,
+        sync_row_index=sync_row_index,
+        azure_rgb_seq=azure_rgb_seq,
+        azure_depth_seq=azure_depth_seq,
+        robot_seq=robot_seq,
+        rgb_path=rgb_path,
+        depth_path=depth_path,
+        rgb=rgb,
+        depth=depth,
+        sync_row=row.to_dict(),
+        camera_matrix=camera_matrix,
+        rvec=rvec,
+        tvec=tvec,
+        table_corners_px=TABLE_CORNERS_PX.copy(),
+        table_size_m=TABLE_SIZE_M,
+        robot_base_world=ROBOT_BASE_WORLD.copy(),
+        robot_base_yaw_rad=float(ROBOT_BASE_YAW_RAD),
+        box_mask=box_mask,
+        teddy_mask=teddy_mask,
+        box_world_polygon=box_world_polygon,
+        teddy_world_center=teddy_world_center,
+        teddy_world_extent=teddy_world_extent,
+        table_depth_mm=table_depth_mm,
+        box_height_m=box_height_m,
+        teddy_height_m=teddy_height_m,
+    )
+
+
+def solve_table_camera(
+    table_corners_px: np.ndarray, table_size_m: tuple[float, float], focal_px: float
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    width_m, height_m = table_size_m
+    object_points = np.array(
+        [
+            [-width_m / 2, -height_m / 2, 0.0],
+            [width_m / 2, -height_m / 2, 0.0],
+            [width_m / 2, height_m / 2, 0.0],
+            [-width_m / 2, height_m / 2, 0.0],
+        ],
+        dtype=np.float64,
+    )
+    camera_matrix = np.array(
+        [[focal_px, 0.0, 960.0], [0.0, focal_px, 540.0], [0.0, 0.0, 1.0]],
+        dtype=np.float64,
+    )
+    ok, rvec, tvec = cv2.solvePnP(object_points, table_corners_px, camera_matrix, None, flags=cv2.SOLVEPNP_IPPE)
+    if not ok:
+        raise RuntimeError("solvePnP failed for table camera fit")
+    return camera_matrix, rvec, tvec
+
+
+def detect_red_box(rgb: np.ndarray) -> np.ndarray:
+    hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV)
+    h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+    mask = (((h < 12) | (h > 170)) & (s > 60) & (v > 80)).astype(np.uint8)
+    num, labels, stats, centers = cv2.connectedComponentsWithStats(mask, connectivity=8)
+    best = None
+    best_area = -1
+    for i in range(1, num):
+        x, y, w, h_box, area = stats[i]
+        cx, cy = centers[i]
+        if area > 50_000 and x > 900 and y < 700 and area > best_area:
+            best_area = int(area)
+            best = labels == i
+    if best is None:
+        raise RuntimeError("failed to detect red box component")
+    return best
+
+
+def detect_teddy(rgb: np.ndarray) -> np.ndarray:
+    hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV)
+    h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+    mask = ((h > 5) & (h < 22) & (s > 15) & (s < 120) & (v > 110) & (v < 245)).astype(np.uint8)
+    num, labels, stats, centers = cv2.connectedComponentsWithStats(mask, connectivity=8)
+    best = None
+    best_area = -1
+    for i in range(1, num):
+        x, y, w, h_box, area = stats[i]
+        cx, cy = centers[i]
+        if 5_000 < area < 50_000 and 850 < cx < 1250 and 350 < cy < 700 and area > best_area:
+            best_area = int(area)
+            best = labels == i
+    if best is None:
+        raise RuntimeError("failed to detect teddy component")
+    return best
+
+
+def estimate_table_depth_mm(depth: np.ndarray) -> float:
+    center_region = depth[150:430, 120:520]
+    valid = center_region[center_region > 0]
+    if len(valid) == 0:
+        raise RuntimeError("no valid table depth values")
+    return float(np.percentile(valid, 50))
+
+
+def estimate_height_from_depth(rgb: np.ndarray, depth: np.ndarray, rgb_mask: np.ndarray, table_depth_mm: float) -> float:
+    ys, xs = np.where(rgb_mask)
+    if len(xs) == 0:
+        return 0.0
+    xd = np.clip((xs * depth.shape[1] / rgb.shape[1]).astype(int), 0, depth.shape[1] - 1)
+    yd = np.clip((ys * depth.shape[0] / rgb.shape[0]).astype(int), 0, depth.shape[0] - 1)
+    values = depth[yd, xd]
+    values = values[values > 0]
+    if len(values) == 0:
+        return 0.0
+    # Use a low percentile to reduce the effect of table pixels around the object.
+    object_depth_mm = float(np.percentile(values, 10))
+    return max(0.0, (table_depth_mm - object_depth_mm) / 1000.0)
+
+
+def image_mask_to_world_polygon(mask: np.ndarray, camera_matrix: np.ndarray, rvec: np.ndarray, tvec: np.ndarray) -> np.ndarray:
+    points = np.column_stack(np.where(mask > 0))[:, ::-1].astype(np.float64)
+    rect = cv2.minAreaRect(points.astype(np.float32))
+    corners = cv2.boxPoints(rect).astype(np.float64)
+    return pixels_to_world_on_table(corners, camera_matrix, rvec, tvec)
+
+
+def mask_centroid_and_extent_world(
+    mask: np.ndarray, camera_matrix: np.ndarray, rvec: np.ndarray, tvec: np.ndarray
+) -> tuple[np.ndarray, np.ndarray]:
+    points = np.column_stack(np.where(mask > 0))[:, ::-1].astype(np.float64)
+    world_points = pixels_to_world_on_table(points, camera_matrix, rvec, tvec)
+    center = world_points.mean(axis=0)
+    extent = world_points.max(axis=0) - world_points.min(axis=0)
+    return center, extent
+
+
+def pixels_to_world_on_table(
+    pixels_uv: np.ndarray, camera_matrix: np.ndarray, rvec: np.ndarray, tvec: np.ndarray
+) -> np.ndarray:
+    rotation, _ = cv2.Rodrigues(rvec)
+    camera_position = (-rotation.T @ tvec).reshape(3)
+    inv_camera = np.linalg.inv(camera_matrix)
+    world_points = []
+    for u, v in pixels_uv:
+        ray_cam = inv_camera @ np.array([u, v, 1.0], dtype=np.float64)
+        ray_cam /= np.linalg.norm(ray_cam)
+        ray_world = rotation.T @ ray_cam
+        if abs(ray_world[2]) < 1e-8:
+            continue
+        scale = -camera_position[2] / ray_world[2]
+        world_points.append(camera_position + scale * ray_world)
+    if not world_points:
+        raise RuntimeError("failed to back-project pixels to the table plane")
+    return np.stack(world_points, axis=0)
+
+
+def create_background_inpaint(rgb: np.ndarray) -> np.ndarray:
+    mask = np.zeros(rgb.shape[:2], dtype=np.uint8)
+    polygon = np.array(
+        [[1080, 520], [1919, 520], [1919, 1079], [980, 1079], [980, 850], [1080, 760]],
+        dtype=np.int32,
+    )
+    cv2.fillPoly(mask, [polygon], 255)
+    cv2.rectangle(mask, (1120, 300), (1450, 680), 0, -1)
+    cv2.rectangle(mask, (860, 350), (1180, 700), 0, -1)
+    return cv2.cvtColor(cv2.inpaint(cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR), mask, 7, cv2.INPAINT_TELEA), cv2.COLOR_BGR2RGB)
+
+
+def kinova_fk_points_world(q_deg: np.ndarray, base_world: np.ndarray, base_yaw_rad: float) -> np.ndarray:
+    points, _ = kinova_fk_points_and_tool_pose(q_deg)
+    cy, sy = np.cos(base_yaw_rad), np.sin(base_yaw_rad)
+    base_rotation = np.array([[cy, -sy, 0.0], [sy, cy, 0.0], [0.0, 0.0, 1.0]], dtype=np.float64)
+    return points @ base_rotation.T + base_world.reshape(1, 3)
+
+
+def kinova_fk_points_and_tool_pose(q_deg: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    q = np.deg2rad(np.asarray(q_deg, dtype=np.float64))
+    params = [
+        (np.pi, 0.0, 0.0, 0.0),
+        (np.pi / 2, 0.0, -(0.1564 + 0.1284), q[0]),
+        (np.pi / 2, 0.0, -(0.0054 + 0.0064), q[1] + np.pi),
+        (np.pi / 2, 0.0, -(0.2104 + 0.2104), q[2] + np.pi),
+        (np.pi / 2, 0.0, -(0.0064 + 0.0064), q[3] + np.pi),
+        (np.pi / 2, 0.0, -(0.2084 + 0.1059), q[4] + np.pi),
+        (np.pi / 2, 0.0, 0.0, q[5] + np.pi),
+        (np.pi, 0.0, -(0.1059 + 0.0615), q[6] + np.pi),
+    ]
+
+    def dh(alpha: float, a: float, d: float, theta: float) -> np.ndarray:
+        ca, sa = np.cos(alpha), np.sin(alpha)
+        ct, st = np.cos(theta), np.sin(theta)
+        return np.array(
+            [
+                [ct, -st * ca, st * sa, a * ct],
+                [st, ct * ca, -ct * sa, a * st],
+                [0.0, sa, ca, d],
+                [0.0, 0.0, 0.0, 1.0],
+            ],
+            dtype=np.float64,
+        )
+
+    transform = np.eye(4, dtype=np.float64)
+    points = []
+    for alpha, a, d, theta in params:
+        transform = transform @ dh(alpha, a, d, theta)
+        points.append(transform[:3, 3].copy())
+
+    tool_transform = transform.copy()
+    tool_transform[:3, 3] = tool_transform[:3, 3] + tool_transform[:3, 2] * TOOL_OFFSET_M
+    points.append(tool_transform[:3, 3].copy())
+    return np.stack(points, axis=0), tool_transform
+
+
+def project_world_points(points_world: np.ndarray, camera_matrix: np.ndarray, rvec: np.ndarray, tvec: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    image_points, _ = cv2.projectPoints(points_world.astype(np.float64), rvec, tvec, camera_matrix, None)
+    rotation, _ = cv2.Rodrigues(rvec)
+    camera_points = (rotation @ points_world.T + tvec).T
+    return image_points[:, 0, :], camera_points[:, 2]
+
+
+def draw_robot(
+    image: np.ndarray,
+    q_deg: np.ndarray,
+    camera_matrix: np.ndarray,
+    rvec: np.ndarray,
+    tvec: np.ndarray,
+    base_world: np.ndarray,
+    base_yaw_rad: float,
+    color: tuple[int, int, int] = (232, 242, 250),
+    alpha: float = 1.0,
+) -> np.ndarray:
+    canvas = image.copy()
+    points_world = kinova_fk_points_world(q_deg, base_world, base_yaw_rad)
+    uv, z = project_world_points(points_world, camera_matrix, rvec, tvec)
+
+    overlay = canvas.copy()
+    for p0, p1, depth_value in zip(uv[:-1], uv[1:], z[:-1]):
+        thickness = max(10, int(35 / max(depth_value, 0.25)))
+        cv2.line(overlay, tuple(np.round(p0).astype(int)), tuple(np.round(p1).astype(int)), color, thickness)
+    for point, depth_value in zip(uv, z):
+        radius = max(8, int(18 / max(depth_value, 0.25)))
+        cv2.circle(overlay, tuple(np.round(point).astype(int)), radius, color, -1)
+    if alpha >= 1.0:
+        return overlay
+    return cv2.addWeighted(overlay, alpha, canvas, 1.0 - alpha, 0.0)
+
+
+def render_scene(
+    calibration: SceneCalibration,
+    q_deg: np.ndarray,
+    background: np.ndarray | None = None,
+    color: tuple[int, int, int] = (232, 242, 250),
+    alpha: float = 1.0,
+) -> np.ndarray:
+    if background is None:
+        background = create_background_inpaint(calibration.rgb)
+    return draw_robot(
+        background,
+        q_deg,
+        calibration.camera_matrix,
+        calibration.rvec,
+        calibration.tvec,
+        calibration.robot_base_world,
+        calibration.robot_base_yaw_rad,
+        color=color,
+        alpha=alpha,
+    )
+
+
+def scene_to_jsonable(calibration: SceneCalibration) -> dict[str, Any]:
+    return {
+        "session_root": str(calibration.session_root),
+        "sync_row_index": calibration.sync_row_index,
+        "azure_rgb_seq": calibration.azure_rgb_seq,
+        "azure_depth_seq": calibration.azure_depth_seq,
+        "robot_seq": calibration.robot_seq,
+        "rgb_path": str(calibration.rgb_path),
+        "depth_path": str(calibration.depth_path),
+        "table_corners_px": calibration.table_corners_px.tolist(),
+        "table_size_m": list(calibration.table_size_m),
+        "camera_focal_px": float(calibration.camera_matrix[0, 0]),
+        "rvec": calibration.rvec.reshape(-1).tolist(),
+        "tvec": calibration.tvec.reshape(-1).tolist(),
+        "robot_base_world": calibration.robot_base_world.tolist(),
+        "robot_base_yaw_rad": float(calibration.robot_base_yaw_rad),
+        "sync_row": calibration.sync_row,
+        "table_depth_mm": float(calibration.table_depth_mm),
+        "box_height_m": float(calibration.box_height_m),
+        "teddy_height_m": float(calibration.teddy_height_m),
+        "box_world_polygon": calibration.box_world_polygon.tolist(),
+        "teddy_world_center": calibration.teddy_world_center.tolist(),
+        "teddy_world_extent": calibration.teddy_world_extent.tolist(),
+    }
+
+
+def save_scene_json(calibration: SceneCalibration, path: str | Path) -> None:
+    path = Path(path)
+    path.write_text(json.dumps(scene_to_jsonable(calibration), indent=2))

bundle/train_teddybear_pi05.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+OPENPI_ROOT="${1:-$HOME/workspace/teddybear_openpi/openpi}"
+cd "$OPENPI_ROOT"
+
+export OPENPI_TORCH_DATALOADER_MP_CONTEXT=default
+
+.venv/bin/python -u scripts/train.py \
+  pi05_kinova_teddybear \
+  --exp-name=teddybear_kinova_ft_v4_b24_w12_fork \
+  --batch-size 24 \
+  --num-workers 12