LEADER 05668nam 22010695 450 001 996418268803316 005 20230126211059.0 010 $a981-15-6263-6 024 7 $a10.1007/978-981-15-6263-1 035 $a(CKB)4100000011354835 035 $a(DE-He213)978-981-15-6263-1 035 $a(MiAaPQ)EBC6420173 035 $a(Au-PeEL)EBL6420173 035 $a(OCoLC)1182513908 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/26952 035 $a(PPN)269148825 035 $a(EXLCZ)994100000011354835 100 $a20200721d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection$b[electronic resource] /$fby Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li 205 $a1st ed. 2020. 210 $cSpringer Nature$d2020 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (XVII, 137 p. 50 illus., 44 illus. in color.) 311 $a981-15-6262-8 327 $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. 330 $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. 606 $aRobotics 606 $aAutomation 606 $aStatistics  606 $aControl engineering 606 $aMechatronics 606 $aMachine learning 606 $aMathematical models 606 $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020 606 $aBayesian Inference$3https://scigraph.springernature.com/ontologies/product-market-codes/S18000 606 $aControl, Robotics, Mechatronics$3https://scigraph.springernature.com/ontologies/product-market-codes/T19000 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aMathematical Modeling and Industrial Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M14068 610 $aRobotics and Automation 610 $aBayesian Inference 610 $aControl, Robotics, Mechatronics 610 $aMachine Learning 610 $aMathematical Modeling and Industrial Mathematics 610 $aRobotic Engineering 610 $aControl, Robotics, Automation 610 $aCollaborative Robot Introspection 610 $aNonparametric Bayesian Inference 610 $aAnomaly Monitoring and Diagnosis 610 $aMultimodal Perception 610 $aAnomaly Recovery 610 $aHuman-robot Collaboration 610 $aRobot Safety and Protection 610 $aHidden Markov Model 610 $aRobot Autonomous Manipulation 610 $aopen access 610 $aRobotics 610 $aBayesian inference 610 $aAutomatic control engineering 610 $aElectronic devices & materials 610 $aMachine learning 610 $aMathematical modelling 610 $aMaths for engineers 615 0$aRobotics. 615 0$aAutomation. 615 0$aStatistics . 615 0$aControl engineering. 615 0$aMechatronics. 615 0$aMachine learning. 615 0$aMathematical models. 615 14$aRobotics and Automation. 615 24$aBayesian Inference. 615 24$aControl, Robotics, Mechatronics. 615 24$aMachine Learning. 615 24$aMathematical Modeling and Industrial Mathematics. 676 $a629.892 700 $aZhou$b Xuefeng$4aut$4http://id.loc.gov/vocabulary/relators/aut$0845353 702 $aWu$b Hongmin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aRojas$b Juan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aXu$b Zhihao$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aLi$b Shuai$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418268803316 996 $aNonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection$91886615 997 $aUNISA