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Learning Motor Skills

From Algorithms to Robot Experiments

  • Jens Kober
  • Jan Peters

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 97)

Table of contents

  1. Front Matter
    Pages 1-15
  2. Jens Kober, Jan Peters
    Pages 1-7
  3. Jens Kober, Jan Peters
    Pages 9-67
  4. Jens Kober, Jan Peters
    Pages 69-82
  5. Jens Kober, Jan Peters
    Pages 83-117
  6. Jens Kober, Jan Peters
    Pages 149-160
  7. Jens Kober, Jan Peters
    Pages 161-167
  8. Back Matter
    Pages 169-190

About this book

Introduction

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.

Keywords

Machine Learning Motor Primitives Policy Search Reinforcement Learning Robotics Skill Learning

Authors and affiliations

  • Jens Kober
    • 1
  • Jan Peters
    • 2
  1. 1.CoR-LabUniversität Bielefeld and Honda Research Center (HRI-EU)BielefeldGermany
  2. 2.FB-Informatik, FG-IAS, and Department for Empirical InferenceTechnische Universitaet Darmstadt and Max-Planck Institute for Intelligent SystemsDarmstadtGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-03194-1
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-03193-4
  • Online ISBN 978-3-319-03194-1
  • Series Print ISSN 1610-7438
  • Series Online ISSN 1610-742X
  • Buy this book on publisher's site
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