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Books > Computing & IT > Applications of computing > Artificial intelligence
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Recent Advances in Robot Learning - Machine Learning (Paperback, Softcover reprint of the original 1st ed. 1996)
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Recent Advances in Robot Learning - Machine Learning (Paperback, Softcover reprint of the original 1st ed. 1996)
Series: The Springer International Series in Engineering and Computer Science, 368
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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).
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