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Recent Advances in Robot Learning

Machine Learning
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Description

This work 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. These characteristics of robotics 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.
Release date NZ
June 30th, 1996
Audiences
  • Postgraduate, Research & Scholarly
  • Professional & Vocational
Contributors
  • Edited by Judy A. Franklin
  • Edited by Sebastian Thrun
  • Edited by Tom M. Mitchell
Edition
Reprinted from MACHINE LEARNING, 23:2-3, 1996
Illustrations
IV, 218 p.
Pages
218
Dimensions
155x235x14
ISBN-13
9780792397458
Product ID
9974504

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Books >
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Machine learning
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Computers & Internet >
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Machine learning