Learning Motor Skills: From Algorithms to Robot Experiments 2014 Edition Contributor(s): Kober, Jens (Author), Peters, Jan (Author) |
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ISBN: 3319031937 ISBN-13: 9783319031934 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: December 2013 |
Additional Information |
BISAC Categories: - Technology & Engineering | Robotics - Computers | Intelligence (ai) & Semantics - Technology & Engineering | Electrical |
Dewey: 006.3 |
Series: Springer Tracts in Advanced Robotics |
Physical Information: 0.5" H x 6.14" W x 9.21" (1.03 lbs) 191 pages |
Descriptions, Reviews, Etc. |
Publisher Description: This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor. skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author's doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award. |