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Robot Learning

  • Book
  • © 1993

Overview

Editors:
  1. Jonathan H. Connell

    1. T.J. Watson Research Center, International Business Machines, USA

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  2. Sridhar Mahadevan

    1. T.J. Watson Research Center, International Business Machines, 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 233)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction to Robot Learning

    • Jonathan H. Connell, Sridhar Mahadevan
    Pages 1-17

  3. Knowledge-Based Training of Artificial Neural Networks for Autonomous Robot Driving

    • Dean A. Pomerleau
    Pages 19-43

  4. Learning Multiple Goal Behavior via Task Decomposition and Dynamic Policy Merging

    • Steven Whitehead, Jonas Karlsson, Josh Tenenberg
    Pages 45-78

  5. Memory-based Reinforcement Learning: Converging with Less Data and Less Real Time

    • Andrew W. Moore, Christopher G. Atkeson
    Pages 79-103

  6. Rapid Task Learning for Real Robots

    • Jonathan H. Connell, Sridhar Mahadevan
    Pages 105-139

  7. The Semantic Hierarchy in Robot Learning

    • Benjamin Kuipers, Richard Froom, Wan-Yik Lee, David Pierce
    Pages 141-170

  8. Uncertainty in Graph-Based Map Learning

    • Thomas Dean, Kenneth Basye, Leslie Kaelbling
    Pages 171-192

  9. Real Robots, Real Learning Problems

    • Rodney A. Brooks, Maja J. Mataric
    Pages 193-213

  10. Back Matter

    Pages 215-240
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Keywords

About this book

Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action.
Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.

Editors and Affiliations

  • T.J. Watson Research Center, International Business Machines, USA

    Jonathan H. Connell, Sridhar Mahadevan

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