pipeline_tag: robotics
library_name: lerobot
license: mit
Robot Learning: A Tutorial
This repository contains a model checkpoint associated with the tutorial paper "Robot Learning: A Tutorial". This tutorial navigates the landscape of modern robot learning, covering foundational principles from Reinforcement Learning and Behavioral Cloning to generalist, language-conditioned models, with practical examples implemented using the Hugging Face lerobot library.
Abstract
Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecedented capabilities in autonomous systems. This tutorial navigates the landscape of modern robot learning, charting a course from the foundational principles of Reinforcement Learning and Behavioral Cloning to generalist, language-conditioned models capable of operating across diverse tasks and even robot embodiments. This work is intended as a guide for researchers and practitioners, and our goal is to equip the reader with the conceptual understanding and practical tools necessary to contribute to developments in robot learning, with ready-to-use examples implemented in lerobot.
Project Page
Find more information and a demo at the official project page: https://huggingface.co/spaces/lerobot/robot-learning-tutorial
Code Repository
The full tutorial source code and examples, which utilize this model, can be found on GitHub: https://github.com/fracapuano/robot-learning-tutorial
Usage
For detailed instructions, installation guides, and code examples on how to train and use diffusion policies and other models within the lerobot framework, please refer to the official tutorial repository on GitHub.
Citation
If you find this work useful, please consider citing the paper:
@misc{fracapuano2025robot,
title={{Robot Learning: A Tutorial}},
author={Francesco Capuano and Kuan Fang and Igor Mordatch and Olivier Pietquin and Scott Niekum and Shuran Song and Jeff Clune and Pieter Abbeel and Chelsea Finn and Sergey Levine and Jeannette Bohg and Peter Stone and Anca Dragan},
year={2025},
eprint={2510.12403},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2510.12403},
}