LeRobot Documentation — Real-World Robot Learning with PyTorch
by Hugging Face
The teleoperate-record-train-deploy loop for real robots, in the same PyTorch idiom you already use for LLMs.
Overview
The LeRobot docs are organised around one workflow — Teleoperate, Record, Train, Deploy — and every section maps onto a stage of it. Get Started covers installation (`pip install lerobot`) and branches three ways: you have a robot, you have no hardware yet, or you want to contribute. The hardware track documents supported platforms including the flagship low-cost SO-101 arm, LeKiwi mobile base, Koch v1.1, HopeJR, OpenARM, Reachy 2 and the Unitree G1 humanoid, plus keyboard, gamepad and phone teleoperation. The no-hardware track is genuinely complete: you can train on any LeRobotDataset streamed from the Hugging Face Hub, evaluate against the LIBERO (130+ VLA tasks) and Meta-World benchmarks, or run the free Colab notebooks. Policy pages document imitation learning (ACT, Diffusion Policy, VQ-BeT, Multitask DiT), reinforcement learning (HIL-SERL, TDMPC), vision-language-action models (π₀, π₀.5, GR00T N1.7, SmolVLA, XVLA, EO-1, WALL-OSS) and world models (VLA-JEPA, FastWAM). Supporting guides carry the parts tutorials usually omit — a CLI cheat sheet, a Compute & Hardware guide telling you which policy fits your GPU and how long training actually takes, the LeRobotDataset v3 format (chunked episodes, streaming, Parquet metadata), camera troubleshooting, and LeLab, a browser GUI for the same workflow. Apache-2.0, ~27.6k GitHub stars, maintained by the Hugging Face robotics team.
At a Glance
- Topic
- ML
- Level
- Intermediate
- Format
- Documentation
- Cost
- Free
- Duration
- ~4-6 hours for the core tutorials without hardware; days to weeks once a real arm is in the loop
- Provider
- Hugging Face
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Run the full teleoperate, record, train and deploy loop on a physical robot arm
- ✓Record synchronised camera video and action data into the standard LeRobotDataset v3 format
- ✓Train an ACT imitation-learning policy from your own demonstrations as a first end-to-end project
- ✓Fine-tune and deploy vision-language-action models including SmolVLA, π₀, π₀.5 and GR00T N1.7
- ✓Evaluate policies in simulation against the LIBERO and Meta-World manipulation benchmarks
- ✓Choose a policy that fits your GPU budget using the Compute and Hardware guide's training-time tables
- ✓Stream large robot datasets from the Hugging Face Hub instead of downloading hundreds of gigabytes
- ✓Diagnose the failure modes that actually bite beginners, starting with camera lighting and calibration
Highlights
- •The no-hardware path is first-class, not an afterthought — train and benchmark policies before buying an arm
- •Hardware-agnostic Python interface spans a $100 SO-101 arm and a Unitree G1 humanoid with the same CLI
- •Policy coverage is unusually current: π₀.5, GR00T N1.7, XVLA and world models like VLA-JEPA are documented, not just referenced
- •LeRobotDataset v3 handles 400GB+ datasets with chunked episodes and video streaming, which is where most robot-learning tutorials stop
- •27.6k stars, Apache-2.0, and a plugin system that lets you add custom hardware without forking the core library
Who It’s For
Best For
- ✓ML engineers with PyTorch experience who want to move from LLMs into embodied AI
- ✓Researchers evaluating vision-language-action policies against a common benchmark suite
- ✓Hobbyists assembling a low-cost SO-101 or LeKiwi robot and needing a working training pipeline
- ✓Teams standardising robot data collection on a shareable dataset format
Prerequisites
- •Comfortable Python and working familiarity with PyTorch training loops
- •A CUDA GPU for training policies (Colab notebooks work for the tutorials)
- •Optional: a supported robot arm — everything else runs in simulation or on Hub datasets
FAQ
What is LeRobot Documentation — Real-World Robot Learning with PyTorch?
LeRobot is Hugging Face's open-source robotics library and its documentation is the fastest route from transformer engineering into vision-language-action models on physical hardware. It is written for Python and PyTorch developers who have never touched a robot, and it walks the whole loop: teleoperate an arm, record demonstrations as a versioned dataset, train an imitation-learning or VLA policy on it, then deploy that policy back onto the robot. After working through it you can train ACT or SmolVLA on a Hub dataset and evaluate it in simulation without owning any hardware at all.
Is LeRobot Documentation — Real-World Robot Learning with PyTorch free?
LeRobot Documentation — Real-World Robot Learning with PyTorch is free to access.
What level is LeRobot Documentation — Real-World Robot Learning with PyTorch for?
LeRobot Documentation — Real-World Robot Learning with PyTorch is aimed at a intermediate audience. Recommended background: Comfortable Python and working familiarity with PyTorch training loops, A CUDA GPU for training policies (Colab notebooks work for the tutorials), Optional: a supported robot arm — everything else runs in simulation or on Hub datasets.
How long does LeRobot Documentation — Real-World Robot Learning with PyTorch take?
Expect roughly ~4-6 hours for the core tutorials without hardware; days to weeks once a real arm is in the loop. Most learners work through it at their own pace.
What will I learn from LeRobot Documentation — Real-World Robot Learning with PyTorch?
You'll learn: Run the full teleoperate, record, train and deploy loop on a physical robot arm; Record synchronised camera video and action data into the standard LeRobotDataset v3 format; Train an ACT imitation-learning policy from your own demonstrations as a first end-to-end project; Fine-tune and deploy vision-language-action models including SmolVLA, π₀, π₀.5 and GR00T N1.7; Evaluate policies in simulation against the LIBERO and Meta-World manipulation benchmarks; Choose a policy that fits your GPU budget using the Compute and Hardware guide's training-time tables; Stream large robot datasets from the Hugging Face Hub instead of downloading hundreds of gigabytes; Diagnose the failure modes that actually bite beginners, starting with camera lighting and calibration.
Topics
Sources
This page was written from 3 sources, 1 on domains other than huggingface.co.