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Titlebook: Recent Advances in Robot Learning; Machine Learning Judy A. Franklin,Tom M. Mitchell,Sebastian Thrun Book 1996 Kluwer Academic Publishers 1

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書目名稱Recent Advances in Robot Learning
副標題Machine Learning
編輯Judy A. Franklin,Tom M. Mitchell,Sebastian Thrun
視頻videohttp://file.papertrans.cn/823/822980/822980.mp4
叢書名稱The Springer International Series in Engineering and Computer Science
圖書封面Titlebook: Recent Advances in Robot Learning; Machine Learning Judy A. Franklin,Tom M. Mitchell,Sebastian Thrun Book 1996 Kluwer Academic Publishers 1
描述.Recent Advances in Robot Learning. contains seven paperson robot learning written by leading researchers in the field. As theselection of papers illustrates, the field of robot learning is bothactive and diverse. A variety of machine learning methods, rangingfrom inductive logic programming to reinforcement learning, is beingapplied to many subproblems in robot perception and control, oftenwith objectives as diverse as parameter calibration and conceptformulation. .While no unified robot learning framework has yet emerged to cover thevariety of problems and approaches described in these papers and otherpublications, a clear set of shared issues underlies many robotlearning problems. .. .Machine learning, when applied to robotics, issituated: it is embedded into a real-world system that tightlyintegrates perception, decision making and execution. ..Sincerobot learning involves decision making, there is an inherent activelearning issue. ..Robotic domains are usually complex, yet theexpense of using actual robotic hardware often prohibits thecollection of large amounts of training data. ..Most roboticsystems are real-time systems. Decisions must be made within criticalor practical ti
出版日期Book 1996
關(guān)鍵詞industrial robot; learning; machine learning; mobile robot; programming; reinforcement learning; robot; rob
版次1
doihttps://doi.org/10.1007/978-1-4613-0471-5
isbn_softcover978-1-4613-8064-1
isbn_ebook978-1-4613-0471-5Series ISSN 0893-3405
issn_series 0893-3405
copyrightKluwer Academic Publishers 1996
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978-1-4613-8064-1Kluwer Academic Publishers 1996
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Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural system is improved in the sense that certain error measure of the closed-loop system decreases as the network learning process is iterated. These analytical results are confirmed by computer simulation. The effectiveness of the proposed approach is demonstrated through a laboratory experiment.
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