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Learning Motor Skills

From Algorithms to Robot Experiments

Jens Kober, Jan Peters (Autoren)

Buch | Hardcover

XVI, 191 Seiten

2013
Springer International Publishing (Verlag)
978-3-319-03193-4 (ISBN)

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This overview by an award-winning researcher of the ways reinforcement learning can be applied to robotics includes new algorithms and applications. It assesses their success in benchmark tasks such as darts, table tennis, and ball-throwing and bouncing.

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.

Jan Peters war Professor für Agrar-, Sozial- und Mentalitätsgeschichte der Frühen Neuzeit an der Universität Potsdam.

Reinforcement Learning in Robotics: A Survey.- Movement Templates for Learning of Hitting and Batting.- Policy Search for Motor Primitives in Robotics.- Reinforcement Learning to Adjust Parameterized Motor Primitives to New Situations.- Learning Prioritized Control of Motor Primitives.

Erscheint lt. Verlag	9.12.2013
Reihe/Serie	Springer Tracts in Advanced Robotics
Zusatzinfo	XVI, 191 p. 56 illus., 54 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	479 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik ► Elektrotechnik / Energietechnik
	Technik ► Maschinenbau
Schlagworte	machine learning • Motor Primitives • Policy Search • Reinforcement Learning • Robotics • Skill Learning
ISBN-10	3-319-03193-7 / 3319031937
ISBN-13	978-3-319-03193-4 / 9783319031934
Zustand	Neuware