Robot Learning: A Tutorial
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911209324806144 |
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| author | Capuano, Francesco Pascal, Caroline Zouitine, Adil Wolf, Thomas Aractingi, Michel |
| author_facet | Capuano, Francesco Pascal, Caroline Zouitine, Adil Wolf, Thomas Aractingi, Michel |
| contents | 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 $\texttt{lerobot}$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robot Learning: A Tutorial Capuano, Francesco Pascal, Caroline Zouitine, Adil Wolf, Thomas Aractingi, Michel Robotics Machine Learning 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 $\texttt{lerobot}$. |
| title | Robot Learning: A Tutorial |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2510.12403 |