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Learning Motor Skills: From Algorithms to Robot Experiments: 97 (Springer Tracts in Advanced Robotics) - Tapa blanda

Libro 95 de 153: Springer Tracts in Advanced Robotics

Kober, Jens; Peters, Jan

 
9783319377322: Learning Motor Skills: From Algorithms to Robot Experiments: 97 (Springer Tracts in Advanced Robotics)
Tapa blanda
ISBN 10: 3319377329 ISBN 13: 9783319377322
Editorial: Springer, 2016

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2016
Idioma
Inglés
ISBN 10 
3319377329
ISBN 13 
9783319377322
Encuadernación
Tapa blanda
Número de páginas
208
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