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Recent Advances in Robot Learning

  • Judy A. Franklin
  • Tom M. Mitchell
  • Sebastian Thrun

Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 368)

Table of contents

  1. Front Matter
    Pages i-iv
  2. Judy A. Franklin, Tom M. Mitchell, Sebastian Thrun
    Pages 1-3
  3. Scott W. Bennett, Gerald F. Dejong
    Pages 5-45
  4. H. Friedrich, S. Münch, R. Dillmann, S. Bocionek, M. Sassin
    Pages 47-73
  5. C. Baroglio, A. Giordana, M. Kaiser, M. Nuttin, R. Piola
    Pages 105-133
  6. Marcos Salganicoff, Lyle H. Ungar, Ruzena Bajcsy
    Pages 135-162
  7. Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida, Koh Hosoda
    Pages 163-187
  8. Volker Klingspor, Katharina J. Morik, Anke D. Rieger
    Pages 189-216
  9. Back Matter
    Pages 333-334

About this book

Introduction

Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation.
While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems.
  • Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution.
  • Since robot learning involves decision making, there is an inherent active learning issue.
  • Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data.
  • Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints.

These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning.
On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution.
Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).

Keywords

industrial robot learning machine learning mobile robot programming reinforcement learning robot robotics

Editors and affiliations

  • Judy A. Franklin
    • 1
  • Tom M. Mitchell
    • 2
  • Sebastian Thrun
    • 2
  1. 1.GTE LaboratoriesUSA
  2. 2.Carnegie Mellon UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-0471-5
  • Copyright Information Springer-Verlag US 1996
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-7923-9745-8
  • Online ISBN 978-1-4613-0471-5
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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