lerobot/docs/source/policy_pi05_README.md

π₀.₅ (pi05)

This repository contains the Hugging Face port of π₀.₅, adapted from OpenPI by the Physical Intelligence. It is designed as a Vision-Language-Action model with open-world generalization.

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Model Overview

Feature	π₀	π₀.₅
Time Conditioning	Concatenates time with actions via action_time_mlp_*	Uses time_mlp_* for AdaRMS conditioning
AdaRMS	Not used	Used in action expert
Tokenizer Length	48 tokens	200 tokens
Discrete State Input	False (Uses state_proj layer)	True
Parameter Count	Higher (includes state embedding)	Lower (no state embedding)

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Relative Actions

π₀.₅ supports training with relative actions, where the model learns relative offsets from the current robot state instead of absolute joint positions. This mirrors the relative-action transform in OpenPI (DeltaActions) and can improve performance.

How it works

1. During preprocessing, absolute actions are converted to relative offsets: relative = action - state (for selected joints).
2. The relative actions are normalized using statistics computed from the relative distribution.
3. During postprocessing, predicted relative actions are converted back to absolute: absolute = relative + state.

Joints listed in relative_exclude_joints (e.g., gripper) are kept absolute.

Configuration

Parameter	Type	Default	Description
use_relative_actions	bool	False	Enable relative-action training
relative_exclude_joints	list[str]	["gripper"]	Joint names to keep absolute (matched by substring)
action_feature_names	list[str]	None	Auto-populated from dataset metadata at runtime by make_policy

Training example

python -m lerobot.scripts.lerobot_train \
  --policy.type=pi05 \
  --dataset.repo_id=your_org/your_dataset \
  --policy.use_relative_actions=true \
  --policy.relative_exclude_joints='["gripper"]'

When use_relative_actions=true, the training script automatically:

• Computes relative action statistics from the dataset (sampled chunk-level relative actions)
• Replaces the standard action stats with relative stats for normalization
• Broadcasts these stats across all ranks in distributed training

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EMA of the policy weights

OpenPI maintains an exponential moving average of the weights during training (ema_decay=0.99 by default) and keeps the EMA copy for inference. To reproduce this with the LeRobot trainer, enable the EMA shadow with a constant decay:

python -m lerobot.scripts.lerobot_train \
  --policy.type=pi05 \
  --dataset.repo_id=your_org/your_dataset \
  --ema.enable=true \
  --ema.decay=0.99

Checkpoints then contain a directly loadable copy of the EMA weights in pretrained_model_ema/ next to the live ones. Note that the shadow is a full extra copy of the parameters on the GPU. Like OpenPI (which disables EMA in its LoRA configs), EMA is not supported together with PEFT adapters.

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Citation

If you use this work, please cite both OpenPI and the π₀.₅ paper:

@misc{openpi2024,
  author       = {Physical Intelligence Lab},
  title        = {OpenPI: PyTorch Implementation of π0 and π0.5 Policies},
  year         = {2024},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/Physical-Intelligence/openpi}},
  license      = {Apache-2.0}
}

@misc{intelligence2025pi05visionlanguageactionmodelopenworld,
  title        = {π₀.₅: a Vision-Language-Action Model with Open-World Generalization},
  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},
  year         = {2025},
  eprint       = {2504.16054},
  archivePrefix= {arXiv},
  primaryClass = {cs.LG},
  url          = {https://arxiv.org/abs/2504.16054},
}

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License

This port follows the Apache 2.0 License, consistent with the original OpenPI repository.