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

Meet the updated SO100, the SO-101 – Just €114 per arm!

Train it in minutes with a few simple moves on your laptop.

Then sit back and watch your creation act autonomously! 🤯

See the full SO-101 tutorial here.

Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!

Check out the LeKiwi tutorial and bring your robot to life on wheels.

LeRobot: State-of-the-art AI for real-world robotics

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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 to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.

🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.

🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulation environments to get started without assembling a robot. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.

🤗 LeRobot hosts pretrained models and datasets on this Hugging Face community page: huggingface.co/lerobot

Examples of pretrained models on simulation environments

ACT policy on ALOHA env	TDMPC policy on SimXArm env	Diffusion policy on PushT env

Installation

LeRobot works with Python 3.10+ and PyTorch 2.2+.

Environment Setup

Create a virtual environment with Python 3.10 and activate it, e.g. with miniforge:

conda create -y -n lerobot python=3.10
conda activate lerobot

When using conda, install ffmpeg in your environment:

conda install ffmpeg -c conda-forge

NOTE: This usually installs ffmpeg 7.X for your platform compiled with the libsvtav1 encoder. If libsvtav1 is not supported (check supported encoders with ffmpeg -encoders), you can:

• [On any platform] Explicitly install ffmpeg 7.X using:

conda install ffmpeg=7.1.1 -c conda-forge

• [On Linux only] Install ffmpeg build dependencies and compile ffmpeg from source with libsvtav1, and make sure you use the corresponding ffmpeg binary to your install with which ffmpeg.

Install LeRobot 🤗

From Source

First, clone the repository and navigate into the directory:

git clone https://github.com/huggingface/lerobot.git
cd lerobot

Then, install the library in editable mode. This is useful if you plan to contribute to the code.

pip install -e .

It is best practice to use a virtual Python environment:

uv venv && source .venv/bin/activate
uv pip install -e ".[all]"

NOTE: If you encounter build errors, you may need to install additional dependencies (cmake, build-essential, and ffmpeg libs). On Linux, run: sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev. For other systems, see: Compiling PyAV

For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:

• aloha
• xarm
• pusht

For instance, to install 🤗 LeRobot with aloha and pusht, use:

pip install -e ".[aloha, pusht]"

Installation from PyPI

Core Library: Install the base package with: LeRobot can be installed directly from PyPI.

pip install lerobot
lerobot-info

Important

For detailed installation guide, please see the Installation Documentation.

Robots & Control

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.

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.

• Structure: Synchronized MP4 videos (or images) for vision and Parquet files for state/action data.
• HF Hub Integration: Explore thousands of robotics datasets on the Hugging Face Hub.
• Tools: Seamlessly delete episodes, split by indices/fractions, add/remove features, and merge multiple datasets.

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, and Vision-Language-Action (VLA) models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.

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

lerobot-train \
  --policy=act \
  --dataset.repo_id=lerobot/aloha_mobile_cabinet

Category	Models
Imitation Learning	ACT, Diffusion, VQ-BeT
Reinforcement Learning	HIL-SERL, TDMPC & QC-FQL (coming soon)
VLAs Models	Pi0Fast, Pi0.5, GR00T N1.5, SmolVLA, XVLA

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.

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

Resources

• Documentation: The complete guide to tutorials & API.
• Chinese Tutorials: LeRobot+SO-ARM101中文教程-同济子豪兄 Detailed doc for assembling, teleoperate, dataset, train, deploy. Verified by Seed Studio and 5 global hackathon players.
• Discord: Join the LeRobot server to discuss with the community.
• X: Follow us on X to stay up-to-date with the latest developments.
• Robot Learning Tutorial: A free, hands-on course to learn robot learning using LeRobot.

Citation

If you use LeRobot in your research, please cite:

@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 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}
}

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!

_{Built by the LeRobot team at Hugging Face with ❤️}