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  Machine Learning for Robotics: Learning Methods for Robot Motor Skills

Peters, J. (2008). Machine Learning for Robotics: Learning Methods for Robot Motor Skills (J. Peters, Ed.).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C95B-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C95C-F
Genre: Book

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 Creators:
Peters, J1, 2, Author              
Peters, J, Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Autonomous robots have been a vision of robotics, artificial intelligence, and cognitive sciences. An important step towards this goal is to create robots that can learn to accomplish amultitude of different tasks triggered by environmental context and higher-level instruction. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s showed that handcrafted approaches do not suffice and that machine learning is needed. However, off the shelf learning techniques often do not scale into real-time or to the high-dimensional domains of manipulator and humanoid robotics. In this book, we investigate the foundations for a general approach to motor skilllearning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and executionis presented along with novel learning algorithms that can be applied in this setting. The resulting framework is shown to work well both in simulation and on real robots.

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 Dates: 2008-05
 Publication Status: Published in print
 Pages: 107
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: ISBN: 978-3-639-02110-3
URI: http://www.amazon.de/Machine-Learning-Robotics-Methods-Skills/dp/363902110X
BibTex Citekey: 5170
 Degree: -

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