Robot Learning: A Tutorial

• URL: http://arxiv.org/abs/2510.12403v1
• Date: Tue, 14 Oct 2025 11:36:46 GMT
• Title: Robot Learning: A Tutorial
• Authors: Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, Michel Aractingi,
• Abstract summary: This tutorial navigates the landscape of modern robot learning, charting a course from the foundational principles of Reinforcement Learning to generalist, language-conditioned models.<n>Our goal is to equip the reader with the conceptual understanding and practical tools necessary to contribute to developments in robot learning.
• Score: 3.266205778385688
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 $\texttt{lerobot}$.

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