LeRobot

State-of-the-art machine learning for real-world robotics

🤗 LeRobot provides a hardware-agnostic, Python-native interface for controlling real robots - from affordable arms like the SO-ARM101 to full humanoids. Plus the tools to record, store, and share the datasets they generate. Every dataset uses the standardized LeRobotDataset format (synchronized video + action/state data) and can be streamed directly from the Hugging Face Hub.

🤗 On top of that data, LeRobot implements state-of-the-art policies - from lightweight imitation-learning models like ACT to large vision-language-action models like π₀ and SmolVLA - all trainable, shareable, and deployable with the same handful of CLI commands.

The goal: lower the barrier to entry for robotics, so that everyone can contribute to, and benefit from, shared datasets and pretrained models.

How It Works

Teleoperate → Record → Train → Deploy

Teleoperate - control the robot yourself (with a leader arm, keyboard, or phone) so it can learn from your movements.
Record - each demonstration is saved as a dataset: synchronized camera video plus the actions you took.
Train - a policy (the neural network that will control the robot) learns to imitate your demonstrations.
Deploy - run the trained policy on the robot and watch it complete the task on its own.

Get Started

New here? Install LeRobot, then pick your path:

<div class="grid grid-cols-1 md:grid-cols-3 gap-4 my-6"> <div class="border dark:border-gray-700 rounded-lg p-4 shadow"> <div class="text-lg font-semibold mb-2">🔧 I have a robot</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> LeRobot supports a wide range of arms and mobile robots. Popular picks: </p> <ul class="text-gray-700 dark:text-gray-300 text-sm list-disc pl-5 mb-2"> <li> <a href="./so101">SO-101</a> - our flagship, low-cost arm </li> <li> <a href="./lekiwi">LeKiwi</a> - a mobile base with an arm on top </li> <li> <a href="./koch">Koch v1.1</a> - a long-time community favorite </li> <li> or find yours under <strong>Robots</strong> in the sidebar </li> </ul> <p class="text-gray-700 dark:text-gray-300 text-sm"> Once it's assembled and calibrated, record a dataset and train your first policy with the <a href="./il_robots">imitation learning tutorial</a> - or skip the CLI entirely with <a href="./lelab">LeLab</a>, a browser GUI for the same workflow. </p> </div> <div class="border dark:border-gray-700 rounded-lg p-4 shadow"> <div class="text-lg font-semibold mb-2">💻 No hardware yet</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> You can still train and evaluate policies without owning a robot: </p> <ul class="text-gray-700 dark:text-gray-300 text-sm list-disc pl-5 mb-2"> <li> train on an existing <a href="https://huggingface.co/datasets?other=LeRobot"> LeRobot dataset </a> from the Hub </li> <li> evaluate in <a href="./envhub">simulation</a>, against benchmarks like LIBERO or Meta-World </li> <li> try the free <a href="./notebooks">Colab notebooks</a> - nothing to install </li> </ul> </div> <div class="border dark:border-gray-700 rounded-lg p-4 shadow"> <div class="text-lg font-semibold mb-2">🤝 I want to contribute</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> Start with the <a href="./contributing">Contributing guide</a>, then <a href="./bring_your_own_policies">add a new policy</a> or <a href="./integrate_hardware">bring your own hardware</a>. </p> </div> </div>

Explore the Docs

<div class="grid grid-cols-1 md:grid-cols-3 gap-4 my-6"> <a class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg" href="./cheat-sheet" > <div class="font-semibold mb-1">📋 Cheat Sheet</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> Every LeRobot CLI command, copy-paste ready. </p> </a> <a class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg" href="./hardware_guide" > <div class="font-semibold mb-1">🖥️ Compute & Hardware Guide</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> Which policy fits your GPU, and how long training takes. </p> </a> <a class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg" href="./lerobot-dataset-v3" > <div class="font-semibold mb-1">🗂️ LeRobotDataset</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> Load, stream, and visualize robot datasets from the Hub. </p> </a> <a class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg" href="./lelab" > <div class="font-semibold mb-1">🖼 LeLab</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> A browser GUI for calibrating, recording, and training - no CLI required. </p> </a> <a class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg" href="./act" > <div class="font-semibold mb-1">🧠 Policies</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> Start with ACT, our recommended first policy - or browse SmolVLA, π₀, and more in the sidebar. </p> </a> <a class="!no-underline border dark:border-gray-700 rounded-lg p-4 shadow hover:shadow-lg" href="./envhub" > <div class="font-semibold mb-1">🎮 Simulation & Benchmarks</div> <p class="text-gray-700 dark:text-gray-300 text-sm"> Train and evaluate in simulated environments before touching real hardware. </p> </a> </div>

Common Problems

Running into issues? A few of the most frequent ones:

Blurry or unusable camera footage - lighting matters more than resolution. See the Cameras guide.
Build or install errors (cmake, ffmpeg, CUDA) - see the Troubleshooting section of the Installation guide.
Not sure which policy fits your GPU - check the Compute & Hardware Guide.
Still stuck? Ask on Discord - the community (and the LeRobot team) is there to help.