Robot Learning: A Tutorial

Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, Michel Aractingi

Robot Learning: A Tutorial: 146 upvotes on Hugging Face Daily Papers, #5 of 47 papers on 2025-10-15. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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