lerobot 0.7.0

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

The base install is lightweight. Add the extras you need, for example pip install 'lerobot[dataset]' to load datasets, 'lerobot[training]' to train policies, or 'lerobot[core_scripts,feetech]' to record with an SO-101 arm.

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: SO101, OpenARM, LeKiwi, Unitree G1, reBot B601, Koch, HopeJR, OMX, EarthRover, Reachy2, Gamepads, Keyboards, Phones and more.

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, 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.

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

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

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 with lerobot-eval. LeRobot supports standard benchmarks like LIBERO, MetaWorld and more to come. To run a trained policy on a real robot, use lerobot-rollout (see the Policy Deployment Documentation).

# 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

• 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.
• T-Shirt Folding Experiment: An end-to-end demonstration of folding t-shirts with LeRobot.
• LeLab: A web interface for LeRobot — teleoperate, calibrate, record datasets, replay, and train your SO arm from the browser, no CLI required.

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:

@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!