| 書目名稱 | Recent Advances in Robot Learning |
| 副標(biāo)題 | Machine Learning |
| 編輯 | Judy A. Franklin,Tom M. Mitchell,Sebastian Thrun |
| 視頻video | http://file.papertrans.cn/823/822980/822980.mp4 |
| 叢書名稱 | The Springer International Series in Engineering and Computer Science |
| 圖書封面 |  |
| 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 |
| doi | https://doi.org/10.1007/978-1-4613-0471-5 |
| isbn_softcover | 978-1-4613-8064-1 |
| isbn_ebook | 978-1-4613-0471-5Series ISSN 0893-3405 |
| issn_series | 0893-3405 |
| copyright | Kluwer Academic Publishers 1996 |
|Archiver|手機(jī)版|小黑屋|
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