# 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-101 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](https://huggingface.co/lerobot). 🤗 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.
LeRobot supports a wide range of arms and mobile robots. Popular picks:
Once it's assembled and calibrated, record a dataset and train your first policy with the imitation learning tutorial.
You can still train and evaluate policies without owning a robot:
Start with the Contributing guide, then add a new policy or bring your own hardware.
Every LeRobot CLI command, copy-paste ready.
Which policy fits your GPU, and how long training takes.
Load, stream, and visualize robot datasets from the Hub.
A browser GUI for calibrating, recording, and training - no CLI required.
Start with ACT, our recommended first policy - or browse SmolVLA, π₀, and more in the sidebar.
Train and evaluate in simulated environments before touching real hardware.