lerobot README

The project's README from github.com/huggingface/lerobot. As one markdown file: README.md.

LeRobot, Hugging Face Robotics Library

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LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry so that everyone can contribute to and benefit from shared datasets and pretrained models.

🤗 A hardware-agnostic, Python-native interface that standardizes control across diverse platforms, from low-cost arms (SO-100) to humanoids.

🤗 A standardized, scalable LeRobotDataset format (Parquet + MP4 or images) hosted on the Hugging Face Hub, enabling efficient storage, streaming and visualization of massive robotic datasets.

🤗 State-of-the-art policies that have been shown to transfer to the real-world ready for training and deployment.

🤗 Comprehensive support for the open-source ecosystem to democratize physical AI.

Quick Start

LeRobot can be installed directly from PyPI.

pip install lerobot
lerobot-info

[!IMPORTANT] For detailed installation guide, please see the Installation Documentation.

Robots & Control

Reachy 2 Demo

LeRobot provides a unified Robot class interface that decouples control logic from hardware specifics. It supports a wide range of robots and teleoperation devices.

from lerobot.robots.myrobot import MyRobot

# Connect to a robot
robot = MyRobot(config=...)
robot.connect()

# Read observation and send action
obs = robot.get_observation()
action = model.select_action(obs)
robot.send_action(action)

Supported Hardware: SO100, LeKiwi, Koch, HopeJR, OMX, EarthRover, Reachy2, Gamepads, Keyboards, Phones, OpenARM, Unitree G1, reBot B601.

While these devices are natively integrated into the LeRobot codebase, the library is designed to be extensible. You can easily implement the Robot interface to utilize LeRobot's data collection, training, and visualization tools for your own custom robot.

For detailed hardware setup guides, see the Hardware Documentation.

LeRobot Dataset

To solve the data fragmentation problem in robotics, we utilize the LeRobotDataset format.

from lerobot.datasets.lerobot_dataset import LeRobotDataset

# Load a dataset from the Hub
dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")

# Access data (automatically handles video decoding)
episode_index=0
print(f"{dataset[episode_index]['action'].shape=}\n")

Learn more about it in the LeRobotDataset Documentation.

SoTA Models

LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, Vision-Language-Action (VLA) models, World Models, and Reward Models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.

Gr00t Architecture

Training a policy is as simple as running a script configuration:

lerobot-train \
  --policy.type=act \
  --dataset.repo_id=lerobot/aloha_mobile_cabinet
Category Models
Imitation Learning ACT, Diffusion, VQ-BeT, Multitask DiT Policy
Reinforcement Learning HIL-SERL, TDMPC & QC-FQL (coming soon)
VLAs Models Pi0, Pi0Fast, Pi0.5, FineART-VLA, GR00T N1.7, SmolVLA, XVLA, EO-1, MolmoAct2, WALL-OSS, EVO1, DM05
World Models VLA-JEPA, LingBot-VA, FastWAM, LaWAM, FLUX 3 Action
Reward Models SARM, TOPReward, Robometer

Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub.

For detailed policy setup guides, see the Policy Documentation. For GPU/RAM requirements and expected training time per policy, see the Compute Hardware Guide.

Inference & Evaluation

Evaluate your policies in simulation or on real hardware using the unified evaluation script. LeRobot supports standard benchmarks like LIBERO, MetaWorld and more to come.

# Evaluate a policy on the LIBERO benchmark
lerobot-eval \
  --policy.path=lerobot/pi0_libero_finetuned \
  --env.type=libero \
  --env.task=libero_object \
  --eval.n_episodes=10

Learn how to implement your own simulation environment or benchmark and distribute it from the HF Hub by following the EnvHub Documentation.

Third-Party Hardware

Beyond the natively supported hardware, the community maintains a growing ecosystem of plugins for other robots, teleoperators, cameras, and sensors - UFACTORY xArm, Universal Robots UR5e, Franka, AgileX Piper, Trossen WidowX, ARX5, I2RT YAM, GELLO, SpaceMouse, Meta Quest, ROS 2 bridges, tactile and depth cameras, and more.

Plugins are auto-discovered by package name: LeRobot imports any installed package prefixed with lerobot_robot_, lerobot_teleoperator_, or lerobot_camera_. Install one and use the type it registers straight from the CLI:

pip install lerobot_robot_<name> lerobot_teleoperator_<name>

lerobot-record \
  --robot.type=<robot_name> \
  --teleop.type=<teleoperator_name> \
  --dataset.repo_id=${HF_USER}/my-dataset

Browse the full list in the Third-Party Robots & Teleoperators and Third-Party Cameras & Sensors documentation.

Resources

Citation

If you use LeRobot in your project, please cite the GitHub repository to acknowledge the ongoing development and contributors:

@misc{cadene2024lerobot,
    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Meftah, Khalil and Ellerbach, Maxime and Moss, Jess and Wolf, Thomas},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}

If you are referencing our research or the academic paper, please also cite our ICLR publication:

ICLR 2026 Paper 
@inproceedings{cadenelerobot,
  title={LeRobot: An Open-Source Library for End-to-End Robot Learning},
  author={Cadene, Remi and Alibert, Simon and Capuano, Francesco and Aractingi, Michel and Zouitine, Adil and Kooijmans, Pepijn and Choghari, Jade and Russi, Martino and Pascal, Caroline and Palma, Steven and Shukor, Mustafa and Moss, Jess and Soare, Alexander and Aubakirova, Dana and Lhoest, Quentin and Gallou\'edec, Quentin and Wolf, Thomas},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://arxiv.org/abs/2602.22818}
}

Contribute

We welcome contributions from everyone in the community! To get started, please read our CONTRIBUTING.md guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!

SO101 Video

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28.2K ·GitHub
Lerobot
What is this?LeRobot is Hugging Face's open-source project providing models, datasets, and tools for real-world robotics in PyTorch, aiming to lower the barrier to entry for building and deploying robot learning policies.
Is this a Python library I import, or a full framework with its own CLI?It's both: a hardware-agnostic Python library with a unified Robot class interface, plus scripts/CLI tooling for data collection, training, evaluation, and visualization.
How do I install it and start training a policy?Install directly from PyPI, then train a policy by running a script configuration against your chosen hardware or dataset.
What do I need besides the library itself: PyTorch, a GPU, or an actual robot?It's built in pure PyTorch and most policies need a GPU for reasonable training times; you don't strictly need physical hardware since it also supports simulation benchmarks like LIBERO and MetaWorld.
Does it support my specific robot out of the box?Only a defined set of hardware (SO100, Koch, Unitree G1, etc.) is natively integrated; anything else requires implementing the Robot interface yourself or installing a community plugin.
Who is this built for?Robotics researchers and engineers who want to train, evaluate, and share imitation-learning, RL, or VLA policies on real or simulated robots.
What robots are natively supported?SO100, LeKiwi, Koch, HopeJR, OMX, EarthRover, Reachy2, Gamepads, Keyboards, Phones, OpenARM, Unitree G1 and reBot B601 are natively integrated, with community plugins adding support for xArm, UR5e, Franka, Piper, WidowX, ARX5, YAM, GELLO and more.
How is the robot dataset data structured?The LeRobotDataset format pairs synchronized MP4 videos (or images) for vision with Parquet files for state/action data, hosted and streamable from the Hugging Face Hub.
What kinds of models does LeRobot include?It covers imitation learning (ACT, Diffusion, VQ-BeT), reinforcement learning (HIL-SERL), vision-language-action models (Pi0, Pi0.5, GR00T N1.7, SmolVLA), world models (VLA-JEPA) and reward models (SARM).
What is LeLab?LeLab is a browser-based web interface for LeRobot that lets you teleoperate, calibrate, record datasets, replay, and train an SO arm without touching the CLI.
What are similar projects?dreamforge, EasyWAM, Motus2, Zero-WAM, Zetta-Embodiment, flex-pi, Embodied-AI-Guide, S-Space
Which topics is it in?vision-language-action, imitation-learning, physical-ai, robot, world-models, embodied-ai, robotics, simulation, dataset, reinforcement-learning, huggingface, pytorch
Creator
huggingface
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Updated
CommitOct 16Releasev0.6.1Aug 03
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Commits · 90 Days