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020 _a9789811562631
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024 7 _a10.1007/978-981-15-6263-1
_2doi
050 4 _aTJ210.2-211.495
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082 0 4 _a629,892
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100 1 _aZhou, Xuefeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXVII, 137 p. 50 illus., 44 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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
700 1 _aRojas, Juan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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
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