| 书目名称 | Inductive Biases in Machine Learning for Robotics and Control |
| 编辑 | Michael Lutter |
| 视频video | |
| 概述 | Presents recent research on Inductive Biases in Machine Learning for Robotics and Control.Interesting for postgraduates and researchers working or wanting to learn more on robot learning with inductiv |
| 丛书名称 | Springer Tracts in Advanced Robotics |
| 图书封面 |  |
| 描述 | .One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.. |
| 出版日期 | Book 2023 |
| 关键词 | Robotics; Robot Learning; Inductive Biases; Control; Machine Learning |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-37832-4 |
| isbn_softcover | 978-3-031-37834-8 |
| isbn_ebook | 978-3-031-37832-4Series ISSN 1610-7438 Series E-ISSN 1610-742X |
| issn_series | 1610-7438 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 17:30:39
