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LeRobot: Open-Source Robot Learning from Zero to Deployment

The complete LeRobot learning path. In ~10 hours go from installing HuggingFace LeRobot to training and deploying your first pick-and-place policy. Works with any supported robot or simulation.

A structured, sequential path through HuggingFace's LeRobot framework. Install, record demonstrations, train an ACT policy, and deploy it — on any supported robot or entirely in simulation.

Total Time ~10 hours

Difficulty Intermediate

Hardware Any supported robot or MuJoCo simulation

Prerequisites Python basics, Linux terminal basics

Simulation Option Yes — full path completable in MuJoCo

You Will Build A trained ACT pick-and-place policy, runnable on your robot

Before You Start — Check These Prerequisites

  • Comfortable with a Linux terminal (cd, ls, pip install)
  • Python 3.10 or higher installed
  • A supported robot (SO-100, Koch v1.1, OpenArm, DK1, HopeJR, Reachy2) or a simulation environment
  • A free HuggingFace account (for dataset and model hosting)
  • About 10 hours of total time across multiple sessions

Not sure if you qualify? Start with Unit 0 — it is specifically designed to answer that question.

Your Path at a Glance

Complete the units in order. Each unit ends with a completion check — do not move on until you pass it.

Before You Begin: LeRobot SetupRequiredWhat LeRobot is, supported hardware, software requirements, time estimates, and where to get help. No code — pure orientation.~20 min · No code
Install and Verify LeRobotRequiredpip vs source install, hardware config setup, and running a pre-trained ACT policy in MuJoCo simulation to verify your environment.~1 hour
Understand the LeRobot Dataset FormatRequiredDataset structure, episode schema, loading and visualizing existing datasets from HuggingFace Hub. Understand what goes in before you record your own.~1.5 hours
Record Your Own DemonstrationsRequiredConnect your robot or simulation, record demonstrations with the LeRobot record command, and push your dataset to HuggingFace Hub.~2 hours
Train a PolicyRequiredChoose between ACT, Diffusion Policy, and SmolVLA. Train ACT on your dataset, read training logs, and save a checkpoint ready for evaluation.~3 hours
Evaluate Your PolicyRequiredEvaluate in simulation or on your real robot, measure success rate, and diagnose the top failure mode before iterating.~1 hour
Improve and ShareRequiredRun the data flywheel, mix datasets, share your model and dataset on HuggingFace Hub, and plan your next steps with Pi0Fast and SmolVLA.~1.5 hours

Time Breakdown

Unit Topic Time
Unit 0 Orientation ~20 min
Unit 1 Install & Verify ~1 h
Unit 2 Dataset Format ~1.5 h
Unit 3 Record Demos ~2 h
Unit 4 Train Policy ~3 h
Unit 5 Evaluate ~1 h
Unit 6 Improve & Share ~1.5 h
Total ~10 h 20 min

Start Unit 0 — Orientation →

Takes about 20 minutes. No technical content — makes sure you have everything ready before the path begins.