From Motor Learning to Interaction Learning in Robots

  • Olivier Sigaud
  • Jan Peters
Part of the Studies in Computational Intelligence book series (SCI, volume 264)

Table of contents

  1. Front Matter
  2. From Motor Learning to Interaction Learning in Robots

    1. Olivier Sigaud, Jan Peters
      Pages 1-12
  3. Part I: Biologically Inspired Models for Motor Learning

    1. Front Matter
      Pages 13-13
    2. Armin Duff, César Rennó-Costa, Encarni Marcos, Andre L. Luvizotto, Andrea Giovannucci, Marti Sanchez-Fibla et al.
      Pages 15-41
    3. M. Lagarde, P. Andry, P. Gaussier, S. Boucenna, L. Hafemeister
      Pages 43-63
    4. Djordje Mitrovic, Stefan Klanke, Sethu Vijayakumar
      Pages 65-84
    5. Pierre-Yves Oudeyer, Adrien Baranes, Frédéric Kaplan
      Pages 107-146
  4. Part II: Learning Policies for Motor Control

    1. Front Matter
      Pages 147-147
    2. Matteo Fumagalli, Arjan Gijsberts, Serena Ivaldi, Lorenzo Jamone, Giorgio Metta, Lorenzo Natale et al.
      Pages 149-167
    3. Camille Salaün, Vincent Padois, Olivier Sigaud
      Pages 169-192
    4. Duy Nguyen-Tuong, Matthias Seeger, Jan Peters
      Pages 193-207
    5. Marc Toussaint, Christian Goerick
      Pages 227-252
    6. Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick, Sethu Vijayakumar
      Pages 253-291
  5. Part III: Imitation and Interaction Learning

    1. Front Matter
      Pages 311-311
    2. Manuel Lopes, Francisco Melo, Luis Montesano, José Santos-Victor
      Pages 313-355
    3. Rawichote Chalodhorn, Rajesh P. N. Rao
      Pages 357-381
    4. Dana Kulić, Yoshihiko Nakamura
      Pages 383-406

About this book

Introduction

From an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop “From motor to interaction learning in robots” held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium.

Keywords

adaptive control autonom behavior biologically inspired complexity control intelligence learning mobile robot modeling reinforcement learning robot robotics sensing sensor

Editors and affiliations

  • Olivier Sigaud
    • 1
  • Jan Peters
    • 2
  1. 1.Institut des Systèmes Intelligents et de Robotique (CNRS UMR 7222)Université Pierre et Marie Curie PyramidePARIS cedex 05France
  2. 2.Dept. SchölkopfMax-Planck Institute for Biological CyberneticsTübingenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-05181-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-05180-7
  • Online ISBN 978-3-642-05181-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book