000 | 04003nam a22006375i 4500 | ||
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001 | 978-981-15-6263-1 | ||
003 | DE-He213 | ||
005 | 20240508090327.0 | ||
007 | cr nn 008mamaa | ||
008 | 200721s2020 si | s |||| 0|eng d | ||
020 |
_a9789811562631 _9978-981-15-6263-1 |
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024 | 7 |
_a10.1007/978-981-15-6263-1 _2doi |
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050 | 4 | _aTJ210.2-211.495 | |
072 | 7 |
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_aTEC037000 _2bisacsh |
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_aTJFM1 _2thema |
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082 | 0 | 4 |
_a629,892 _223 |
100 | 1 |
_aZhou, Xuefeng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aNonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection _h[electronic resource] / _cby Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2020. |
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300 |
_aXVII, 137 p. 50 illus., 44 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aIntroduction to Robot Introspection -- Nonparametric Bayesian Modeling of Multimodal Time Series -- Incremental Learning Robot Complex Task Representation and Identification -- Nonparametric Bayesian Method for Robot Anomaly Monitoring -- Nonparametric Bayesian Method for Robot Anomaly Diagnose -- Learning Policy for Robot Anomaly Recovery based on Robot. | |
506 | 0 | _aOpen Access | |
520 | _aThis open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students. | ||
650 | 0 | _aRobotics. | |
650 | 0 | _aStatistics . | |
650 | 0 | _aControl engineering. | |
650 | 0 | _aAutomation. | |
650 | 0 | _aMachine learning. | |
650 | 0 | _aMathematical models. | |
650 | 1 | 4 | _aRobotic Engineering. |
650 | 2 | 4 | _aBayesian Inference. |
650 | 2 | 4 | _aControl, Robotics, Automation. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aMathematical Modeling and Industrial Mathematics. |
700 | 1 |
_aWu, Hongmin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aRojas, Juan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aXu, Zhihao. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aLi, Shuai. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811562624 |
776 | 0 | 8 |
_iPrinted edition: _z9789811562648 |
776 | 0 | 8 |
_iPrinted edition: _z9789811562655 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-15-6263-1 |
912 | _aZDB-2-SMA | ||
912 | _aZDB-2-SXMS | ||
912 | _aZDB-2-SOB | ||
999 |
_c37739 _d37739 |