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robot-learning-tutorial

All the source code for "Robot Learning: A Tutorial". Get involved to be featured in the next iteration!

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创建于 2025-08-10 · 更新于 2026-10-15 · 今日第 16456 名
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关键参数与规格
关键参数与规格
参数值
语言TeX
类别Tutorials
子类别ML/AI fundamentals
关键词RL, Robotics
星标数590
分叉数65
贡献者1
创建日期2025-08-10
更新日期2026-10-15
1 天星标数0
7 天星标数1
排名16,456
上次排名6,828
排名变化-9,628
7 天星标排名11,243
上次 7 天星标排名11,297
7 天星标排名变化54
总星标排名16,992
上次总星标排名16,960
总星标排名变化-32
国家France
README 摘录

Robot Learning: A Tutorial This repository contains the source code for the "Robot Learning: A Tutorial" report. This tutorial covers many of the most pressing aspects in modern robot learning, and provides practice examples using , the robot-learning library developed by Hugging Face. You’re more than welcome to contribute to the next edition of the tutorial! Simply open an issue, tag @fracapuano, and start a discussion about the scope and content you’d like to add. md for more

details 😊 All merged pull requests will receive public acknowledgment in the main body of the tutorial. Items marked with an empty in the following Table of Contents are open for community contribution! 2 Code Example: Batching a (Streaming) Dataset [x] 1. 4 Limitations of Dynamics-based Robotics 3. 4 Limitations of RL in Real-World Robotics: Simulators and Reward Design 4. 1 A (Concise) Introduction to Generative Models [x] 4. 2 Action Chunking with Transformers [x] 4. Some Emerging Directions

in Robot Learning [ ] 6. 1 Conclusions License The written content of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4. All source code examples in the directory are licensed under the MIT License.

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fracapuano/robot-learning-tutorial 排名第 16,456。

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README

Robot Learning: A Tutorial

This repository contains the source code for the "Robot Learning: A Tutorial" report. This tutorial covers many of the most pressing aspects in modern robot learning, and provides practice examples using lerobot, the robot-learning library developed by Hugging Face.

You’re more than welcome to contribute to the next edition of the tutorial! Simply open an issue, tag @fracapuano, and start a discussion about the scope and content you’d like to add. Check out CONTRIBUTING.md for more details 😊 All merged pull requests will receive public acknowledgment in the main body of the tutorial. Items marked with an empty [ ] in the following Table of Contents are open for community contribution!

Table of Contents

1. Introduction

  • [x] 1.1 lerobot Dataset
    • [x] 1.1.1 The dataset class design
  • [x] 1.2 Code Example: Batching a (Streaming) Dataset
  • [x] 1.3 Code Example: Collecting Data

2. Classical Robotics

  • [x] 2.1 Explicit and Implicit Models
  • [x] 2.2 Different Types of Motion
  • [x] 2.3 Example: Planar Manipulation
    • [x] 2.3.1 Adding Feedback Loops
  • [x] 2.4 Limitations of Dynamics-based Robotics

3. Robot (Reinforcement) Learning

  • [x] 3.1 A (Concise) Introduction to RL
  • [x] 3.2 Real-world RL for Robotics
  • [x] 3.3 Code Example: Real-world RL
  • [x] 3.4 Limitations of RL in Real-World Robotics: Simulators and Reward Design

4. Robot (Imitation) Learning

  • [x] 4.1 A (Concise) Introduction to Generative Models
    • [x] 4.1.1 Variational Auto-Encoders
    • [x] 4.1.2 Diffusion Models
    • [x] 4.1.3 Flow Matching
  • [x] 4.2 Action Chunking with Transformers
    • [x] 4.2.1 Code Example: Training and Using ACT in Practice
  • [x] 4.3 Diffusion Policy
    • [x] 4.3.1 Code Example: Training and Using Diffusion Policies in Practice
  • [x] 4.4 Optimized Inference
    • [x] 4.4.1 Code Example: Using Async Inference

5. Generalist Robot Policies

  • [x] 5.1 Preliminaries: Models and Data
  • [x] 5.2 Modern VLAs
    • [x] 5.2.1 VLMs for VLAs
  • [x] 5.3 PI0
    • [x] 5.3.1 Code Example: Using PI0
  • [x] 5.4 SmolVLA
    • [x] 5.4.1 Code Example: Using SmolVLA
    • [ ] 5.4.2 Code Example: Training SmolVLA
  • [ ] 5.5 GR00T (1/2)
    • [ ] 5.5.1 Code Example: Using GR00T
  • [ ] 5.6 PI05
    • [ ] 5.6.1 Code Example: Using PI05
  • [ ] Large-scale datasets
    • [ ] Open-X
    • [ ] DROID
    • [ ] BEHAVIOR

6. Some Emerging Directions in Robot Learning

  • [ ] 6.1 Post-training VLAs
    • [ ] 6.1.1 From Imitation to Refinement
    • [ ] 6.1.2 EXPO
  • [ ] 6.2 World Models for robotics
    • [ ] 6.2.1 Cosmos
    • [ ] 6.2.2 World Models (1X)
    • [ ] 6.2.3 Sima and Genie 1

7. Conclusions

  • [x] 7.1 Conclusions

License

The written content of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

All source code examples in the snippets/ directory are licensed under the MIT License.

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