π₀ (pi0)

This repository contains the Hugging Face port of π₀, adapted from OpenPI by the Physical Intelligence. It is designed as a Vision-Language-Action model for general robot control.

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)

Relative Actions

π₀ 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

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

bash

python -m lerobot.scripts.lerobot_train \
  --policy.type=pi0 \
  --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

Recomputing stats for an existing dataset

If you want to precompute relative action stats offline, use recompute_stats from lerobot.datasets.dataset_tools:

python

from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.dataset_tools import recompute_stats

dataset = LeRobotDataset("your_org/your_dataset")
dataset = recompute_stats(
    dataset,
    relative_action=True,
    relative_exclude_joints=["gripper"],
)

Citation

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

bibtex

@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{black2024pi0visionlanguageactionflowmodel,
  title        = {π₀: A Vision-Language-Action Flow Model for General Robot Control},
  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},
  year         = {2024},
  eprint       = {2410.24164},
  archivePrefix= {arXiv},
  primaryClass = {cs.LG},
  url          = {https://arxiv.org/abs/2410.24164},
}

License

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