> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/huggingface/lerobot/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction to LeRobot

> An open-source library for end-to-end robot learning in PyTorch

## What is LeRobot?

LeRobot is a state-of-the-art machine learning library for real-world robotics, built by Hugging Face. It aims to provide models, datasets, and tools for real-world robotics in PyTorch, lowering the barrier to entry so that everyone can contribute to and benefit from shared datasets and pretrained models.

<Note>
  LeRobot democratizes physical AI by providing a hardware-agnostic, Python-native interface that standardizes control across diverse platforms, from low-cost arms (SO-100) to humanoids.
</Note>

## Key Features

<CardGroup cols={2}>
  <Card title="Unified Robot Interface" icon="robot">
    A hardware-agnostic Python interface that standardizes control across diverse platforms, from low-cost arms (SO-100) to humanoids like Reachy2 and Unitree G1.
  </Card>

  <Card title="Standardized Datasets" icon="database">
    LeRobotDataset format (Parquet + MP4) hosted on Hugging Face Hub enables efficient storage, streaming, and visualization of massive robotic datasets.
  </Card>

  <Card title="State-of-the-Art Policies" icon="brain">
    Implementations of cutting-edge policies including ACT, Diffusion, VQ-BeT, HIL-SERL, Pi0, and Vision-Language-Action models like GR00T and SmolVLA.
  </Card>

  <Card title="Comprehensive Ecosystem" icon="circle-nodes">
    Full support for data collection, training, evaluation, and deployment with seamless integration to Hugging Face Hub.
  </Card>
</CardGroup>

## Supported Hardware

LeRobot natively integrates with a wide range of robotic hardware:

* **Robot Arms**: SO100, SO101, Koch, OpenARM, OMX
* **Mobile Manipulators**: LeKiwi, HopeJR, EarthRover
* **Humanoids**: Reachy2, Unitree G1
* **Teleoperation Devices**: Gamepads, Keyboards, Phones

<Tip>
  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.
</Tip>

## Architecture Overview

LeRobot follows a modular architecture with three main components:

### 1. Robot Control

The unified `Robot` class interface decouples control logic from hardware specifics:

```python theme={null}
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)
```

Every robot implementation provides:

* `observation_features`: Describes the structure of sensor data
* `action_features`: Describes the structure of control commands
* `get_observation()`: Returns current robot state and sensor readings
* `send_action()`: Executes control commands

### 2. Dataset Management

LeRobotDataset provides a PyTorch-compatible dataset class with:

```python theme={null}
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=}")
```

* **Efficient Storage**: Synchronized MP4 videos for vision + Parquet files for state/action
* **Hub Integration**: Explore thousands of datasets on [Hugging Face Hub](https://huggingface.co/lerobot)
* **Powerful Tools**: Delete episodes, split datasets, add/remove features, and merge multiple datasets

### 3. Policy Training & Evaluation

LeRobot implements state-of-the-art policies covering:

<Tabs>
  <Tab title="Imitation Learning">
    * **ACT** (Action Chunking with Transformers): Fine-grained bimanual manipulation
    * **Diffusion Policy**: Diffusion models for behavior cloning
    * **VQ-BeT**: Vector-quantized behavior transformers
  </Tab>

  <Tab title="Reinforcement Learning">
    * **HIL-SERL**: Human-in-the-loop robot learning
    * **TDMPC**: Temporal difference model predictive control
  </Tab>

  <Tab title="Vision-Language-Action">
    * **Pi0 & Pi0Fast**: Flow matching policies for robotics
    * **Pi0.5**: Advanced VLA with improved reasoning
    * **GR00T N1.5**: NVIDIA's humanoid foundation model
    * **SmolVLA**: Efficient vision-language-action model
    * **XVLA**: Cross-embodiment VLA
  </Tab>
</Tabs>

Training a policy is simple:

```bash theme={null}
lerobot-train \
  --policy=act \
  --dataset.repo_id=lerobot/aloha_mobile_cabinet
```

## Research Foundation

LeRobot is based on peer-reviewed research published at ICLR 2026:

```bibtex theme={null}
@inproceedings{cadenelerobot,
  title={LeRobot: An Open-Source Library for End-to-End Robot Learning},
  author={Cadene, Remi and Alibert, Simon and Capuano, Francesco and others},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://arxiv.org/abs/2602.22818}
}
```

## Use Cases

LeRobot is designed for:

* **Researchers**: Experiment with state-of-the-art robot learning algorithms
* **Engineers**: Deploy trained policies on real hardware with minimal friction
* **Hobbyists**: Build and train robots with low-cost hardware
* **Educators**: Teach robot learning with accessible tools and datasets

## Next Steps

<CardGroup cols={2}>
  <Card title="Installation" icon="download" href="/installation">
    Install LeRobot and set up your environment
  </Card>

  <Card title="Quick Start" icon="rocket" href="/quickstart">
    Get started with your first robot learning example
  </Card>

  <Card title="Hardware Setup" icon="wrench" href="/integrate_hardware">
    Set up your robot hardware with LeRobot
  </Card>

  <Card title="Dataset Documentation" icon="book" href="/lerobot-dataset-v3">
    Learn about the LeRobotDataset format
  </Card>
</CardGroup>

## Community & Support

* **Discord**: Join the [LeRobot Discord server](https://discord.gg/q8Dzzpym3f) to discuss with the community
* **GitHub**: Report issues and contribute at [github.com/huggingface/lerobot](https://github.com/huggingface/lerobot)
* **X/Twitter**: Follow [@LeRobotHF](https://x.com/LeRobotHF) for updates
* **Tutorial**: Take the free [Robot Learning Tutorial](https://huggingface.co/spaces/lerobot/robot-learning-tutorial)

<Note>
  Built by the LeRobot team at Hugging Face with contributions from the open-source robotics community.
</Note>