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Keywords: Machine Learning Robotics Artificial Intelligence

Recent Advances in Robot Learning

edited by
Judy A. Franklin
GTE Laboratories, Waltham, MA, USA
Tom M. Mitchell
Carnegie Mellon University, Pittsburgh, PA, USA
Sebastian Thrun
Carnegie Mellon University, Pittsburgh, PA, USA

Reprinted from MACHINE LEARNING, 23:2-3

THE KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE
VOLUME 368

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).

Kluwer Academic Publishers, Boston

Date of publishing: June 1996
224 pp.
Hardbound
ISBN: 0-7923-9745-2
Prices:

NLG: 175.00
USD: 94.00
GBP: 66.75

============================================================================= ORDER FORM

Author: Judy A. Franklin
Title: Recent Advances in Robot Learning
( ) Hardbound / ISBN: 0-7923-9745-2
NLG: 175.00 USD: 94.00 GBP: 66.75

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