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chore(docs): no policy readme in src code (#3286)
* chore(docs): move policies readme out of src code * chore(docs): create symlink for policy readme
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Steven Palma
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# π₀.₅ (pi05)
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This repository contains the Hugging Face port of **π₀.₅**, adapted from [OpenPI](https://github.com/Physical-Intelligence/openpi) by the Physical Intelligence.
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It is designed as a **Vision-Language-Action model with open-world generalization**.
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---
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## Model Overview
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| Feature | π₀ | π₀.₅ |
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| -------------------- | ------------------------------------------------------ | ----------------------------------------- |
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| Time Conditioning | Concatenates time with actions via `action_time_mlp_*` | Uses `time_mlp_*` for AdaRMS conditioning |
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| AdaRMS | Not used | Used in action expert |
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| Tokenizer Length | 48 tokens | 200 tokens |
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| Discrete State Input | False (Uses `state_proj` layer) | True |
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| Parameter Count | Higher (includes state embedding) | Lower (no state embedding) |
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---
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## Relative Actions
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π₀.₅ supports training with **relative actions**, where the model learns relative offsets
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from the current robot state instead of absolute joint positions. This mirrors the
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relative-action transform in OpenPI (`DeltaActions`) and can improve performance.
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### How it works
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1. **During preprocessing**, absolute actions are converted to relative offsets:
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`relative = action - state` (for selected joints).
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2. The relative actions are normalized using statistics computed from the relative distribution.
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3. **During postprocessing**, predicted relative actions are converted back to absolute:
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`absolute = relative + state`.
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Joints listed in `relative_exclude_joints` (e.g., gripper) are kept absolute.
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### Configuration
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| Parameter | Type | Default | Description |
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| ------------------------- | ----------- | ------------- | ---------------------------------------------------------------- |
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| `use_relative_actions` | `bool` | `False` | Enable relative-action training |
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| `relative_exclude_joints` | `list[str]` | `["gripper"]` | Joint names to keep absolute (matched by substring) |
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| `action_feature_names` | `list[str]` | `None` | Auto-populated from dataset metadata at runtime by `make_policy` |
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### Training example
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```bash
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python -m lerobot.scripts.lerobot_train \
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--policy.type=pi05 \
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--dataset.repo_id=your_org/your_dataset \
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--policy.use_relative_actions=true \
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--policy.relative_exclude_joints='["gripper"]'
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```
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When `use_relative_actions=true`, the training script automatically:
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- Computes relative action statistics from the dataset (sampled chunk-level relative actions)
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- Replaces the standard action stats with relative stats for normalization
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- Broadcasts these stats across all ranks in distributed training
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---
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## Citation
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If you use this work, please cite both **OpenPI** and the π₀.₅ paper:
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```bibtex
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@misc{openpi2024,
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author = {Physical Intelligence Lab},
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title = {OpenPI: PyTorch Implementation of π0 and π0.5 Policies},
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year = {2024},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/Physical-Intelligence/openpi}},
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license = {Apache-2.0}
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}
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@misc{intelligence2025pi05visionlanguageactionmodelopenworld,
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title = {π₀.₅: a Vision-Language-Action Model with Open-World Generalization},
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author = {Physical Intelligence and Kevin Black and Noah Brown and James Darpinian and Karan Dhabalia and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Manuel Y. Galliker and Dibya Ghosh and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and James Tanner and Quan Vuong and Homer Walke and Anna Walling and Haohuan Wang and Lili Yu and Ury Zhilinsky},
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year = {2025},
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eprint = {2504.16054},
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archivePrefix= {arXiv},
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primaryClass = {cs.LG},
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url = {https://arxiv.org/abs/2504.16054},
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}
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```
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---
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## License
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This port follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).
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# π₀ (pi0)
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This repository contains the Hugging Face port of **π₀**, adapted from [OpenPI](https://github.com/Physical-Intelligence/openpi) by the Physical Intelligence.
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It is designed as a **Vision-Language-Action model for general robot control**.
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---
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## Model Overview
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| Feature | π₀ | π₀.₅ |
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| -------------------- | ------------------------------------------------------ | ----------------------------------------- |
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| Time Conditioning | Concatenates time with actions via `action_time_mlp_*` | Uses `time_mlp_*` for AdaRMS conditioning |
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| AdaRMS | Not used | Used in action expert |
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| Tokenizer Length | 48 tokens | 200 tokens |
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| Discrete State Input | False (Uses `state_proj` layer) | True |
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| Parameter Count | Higher (includes state embedding) | Lower (no state embedding) |
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---
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## Relative Actions
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π₀ supports training with **relative actions**, where the model learns relative offsets
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from the current robot state instead of absolute joint positions. This mirrors the
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relative-action transform in OpenPI (`DeltaActions`) and can improve performance.
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### How it works
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1. **During preprocessing**, absolute actions are converted to relative offsets:
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`relative = action - state` (for selected joints).
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2. The relative actions are normalized using statistics computed from the relative distribution.
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3. **During postprocessing**, predicted relative actions are converted back to absolute:
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`absolute = relative + state`.
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Joints listed in `relative_exclude_joints` (e.g., gripper) are kept absolute.
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### Configuration
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| Parameter | Type | Default | Description |
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| ------------------------- | ----------- | ------------- | ---------------------------------------------------------------- |
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| `use_relative_actions` | `bool` | `False` | Enable relative-action training |
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| `relative_exclude_joints` | `list[str]` | `["gripper"]` | Joint names to keep absolute (matched by substring) |
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| `action_feature_names` | `list[str]` | `None` | Auto-populated from dataset metadata at runtime by `make_policy` |
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### Training example
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```bash
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python -m lerobot.scripts.lerobot_train \
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--policy.type=pi0 \
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--dataset.repo_id=your_org/your_dataset \
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--policy.use_relative_actions=true \
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--policy.relative_exclude_joints='["gripper"]'
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```
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When `use_relative_actions=true`, the training script automatically:
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- Computes relative action statistics from the dataset (sampled chunk-level relative actions)
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- Replaces the standard action stats with relative stats for normalization
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- Broadcasts these stats across all ranks in distributed training
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### Recomputing stats for an existing dataset
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If you want to precompute relative action stats offline, use `recompute_stats` from
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`lerobot.datasets.dataset_tools`:
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```python
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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from lerobot.datasets.dataset_tools import recompute_stats
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dataset = LeRobotDataset("your_org/your_dataset")
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dataset = recompute_stats(
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dataset,
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relative_action=True,
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relative_exclude_joints=["gripper"],
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)
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```
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---
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## Citation
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If you use this work, please cite both **OpenPI** and the π₀ paper:
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```bibtex
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@misc{openpi2024,
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author = {Physical Intelligence Lab},
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title = {OpenPI: PyTorch Implementation of π0 and π0.5 Policies},
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year = {2024},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/Physical-Intelligence/openpi}},
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license = {Apache-2.0}
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}
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@misc{black2024pi0visionlanguageactionflowmodel,
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title = {π₀: A Vision-Language-Action Flow Model for General Robot Control},
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author = {Kevin Black and Noah Brown and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Lucy Xiaoyang Shi and James Tanner and Quan Vuong and Anna Walling and Haohuan Wang and Ury Zhilinsky},
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year = {2024},
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eprint = {2410.24164},
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archivePrefix= {arXiv},
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primaryClass = {cs.LG},
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url = {https://arxiv.org/abs/2410.24164},
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}
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```
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---
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## License
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This port follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).
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# Real-Time Chunking (RTC)
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This module contains the LeRobot implementation of **Real-Time Chunking (RTC)**, an inference-time technique for flow-matching based policies.
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**Note**: RTC is not a policy itself, but rather an inference enhancement that works with flow-matching based policies including [π₀](../pi0/), [π₀.₅](../pi05/), and [SmolVLA](../smolvla/).
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---
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## Citation
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If you use Real-Time Chunking in your work, please cite:
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```bibtex
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@misc{openpi2024,
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author = {Physical Intelligence Lab},
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title = {OpenPI: PyTorch Implementation of π0 and π0.5 Policies},
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year = {2024},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/Physical-Intelligence/openpi}},
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license = {Apache-2.0}
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}
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@misc{black2025realtimeexecutionactionchunking,
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title={Real-Time Execution of Action Chunking Flow Policies},
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author={Kevin Black and Manuel Y. Galliker and Sergey Levine},
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year={2025},
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eprint={2506.07339},
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archivePrefix={arXiv},
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primaryClass={cs.RO},
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url={https://arxiv.org/abs/2506.07339},
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}
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```
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---
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## License
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This implementation follows the **Apache 2.0 License**, consistent with the LeRobot project.
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## Paper
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https://arxiv.org/abs/2509.25358
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## Citation
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```bibtex
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@article{chen2025sarm,
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title={SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation},
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author={Chen, Qianzhong and Yu, Justin and Schwager, Mac and Abbeel, Pieter and Shentu, Yide and Wu, Philipp},
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journal={arXiv preprint arXiv:2509.25358},
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year={2025}
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}
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```
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