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Robot Learning is now a well-developed research area. This book explores the full scope of the fi eld which encompasses Evolutionary Techniques, Reinforcement Learning, Hidden Markov Models, Uncertainty, Action Models, Navigation and Biped Locomotion, etc. Robot Learning in realistic environments requires novel algorithms for learning to identify important events in the stream of sensory inputs and to temporarily memorize them in adaptive, dynamic, internal states, until the memories can help to compute proper control actions. The book covers many of such algorithms in its 8 chapters.
This book is primarily intended for the use in a postgraduate course. To use it effectively, students should have some background knowledge in both Computer Science and Mathematics. Because of its comprehensive coverage and algorithms, it is useful as a primary reference for the graduate students and professionals wishing to branch out beyond their subfi eld. Given the interdisciplinary nature of the robot learning problem, the book may be of interest to wide variety of readers, including computer scientists, roboticists, mechanical engineers, psychologists, ethologists, mathematicians, etc.
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