π₀ (pi0)#

← Tutorial Library · policies and Models

This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

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#

  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=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:

from lerobot.datasets import LeRobotDataset, 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:

@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.