huggingface / lerobot

**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.
 [code] 
> [!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.
 [code] 
**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.
- **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.
 [code] 
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:
 [code] 
| Category | Models |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Imitation Learning** | ACT, D