EPISODE · Oct 16, 2025 · 23 MIN
Robot Learning: A Tutorial
from Daily Paper Cast · host Jingwen Liang, Gengyu Wang
🤗 Upvotes: 44 | cs.RO, cs.LG Authors: Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, Michel Aractingi Title: Robot Learning: A Tutorial Arxiv: http://arxiv.org/abs/2510.12403v1 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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🤗 Upvotes: 44 | cs.RO, cs.LG Authors: Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, Michel Aractingi Title: Robot Learning: A Tutorial Arxiv: http://arxiv.org/abs/2510.12403v1 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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Robot Learning: A Tutorial
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