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Recent Advances in Robot Learning

Machine Learning

  • Book
  • © 1996

Overview

Editors:
  1. Judy A. Franklin

    1. GTE Laboratories, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Tom M. Mitchell

    1. Carnegie Mellon University, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Sebastian Thrun

    1. Carnegie Mellon University, USA

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    You can also search for this editor in PubMed   Google Scholar

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

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-iv
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  2. Introduction

    • Judy A. Franklin, Tom M. Mitchell, Sebastian Thrun
    Pages 1-3

  3. Real-World Robotics: Learning to Plan for Robust Execution

    • Scott W. Bennett, Gerald F. Dejong
    Pages 5-45

  4. Robot Programming by Demonstration (RPD): Supporting the Induction by Human Interaction

    • H. Friedrich, S. Münch, R. Dillmann, S. Bocionek, M. Sassin
    Pages 47-73

  5. Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks

    • Peter C. Y. Chen, James K. Mills, Kenneth C. Smith
    Pages 75-104

  6. Learning Controllers for Industrial Robots

    • C. Baroglio, A. Giordana, M. Kaiser, M. Nuttin, R. Piola
    Pages 105-133

  7. Active Learning for Vision-Based Robot Grasping

    • Marcos Salganicoff, Lyle H. Ungar, Ruzena Bajcsy
    Pages 135-162

  8. Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning

    • Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida, Koh Hosoda
    Pages 163-187

  9. Learning Concepts from Sensor Data of a Mobile Robot

    • Volker Klingspor, Katharina J. Morik, Anke D. Rieger
    Pages 189-216

  10. Back Matter

    Pages 333-334
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Keywords

About this book

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).

Editors and Affiliations

  • GTE Laboratories, USA

    Judy A. Franklin

  • Carnegie Mellon University, USA

    Tom M. Mitchell, Sebastian Thrun

Bibliographic Information

  • Book Title: Recent Advances in Robot Learning

  • Book Subtitle: Machine Learning

  • Editors: Judy A. Franklin, Tom M. Mitchell, Sebastian Thrun

  • Series Title: The Springer International Series in Engineering and Computer Science

  • DOI: https://doi.org/10.1007/978-1-4613-0471-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Kluwer Academic Publishers 1996

  • Hardcover ISBN: 978-0-7923-9745-8Published: 30 June 1996

  • Softcover ISBN: 978-1-4613-8064-1Published: 17 September 2011

  • eBook ISBN: 978-1-4613-0471-5Published: 06 December 2012

  • Series ISSN: 0893-3405

  • Edition Number: 1

  • Number of Pages: IV, 218

  • Topics: Artificial Intelligence

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