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dc.contributor.authorZhou, Xuefeng
dc.contributor.authorWu, Hongmin
dc.contributor.authorRojas, Juan
dc.contributor.authorXu, Zhihao
dc.contributor.authorLi, Shuai
dc.date.accessioned2020-08-13T11:54:30Z
dc.date.available2020-08-13T11:54:30Z
dc.date.issued2020
dc.identifierONIX_20200813_9789811562631_42
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/41300
dc.description.abstractThis 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering::TJFM1 Roboticsen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics::PBTB Bayesian inferenceen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineeringen_US
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learningen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modellingen_US
dc.subject.otherRobotics and Automation
dc.subject.otherBayesian Inference
dc.subject.otherControl, Robotics, Mechatronics
dc.subject.otherMachine Learning
dc.subject.otherMathematical Modeling and Industrial Mathematics
dc.subject.otherRobotic Engineering
dc.subject.otherControl, Robotics, Automation
dc.subject.otherCollaborative Robot Introspection
dc.subject.otherNonparametric Bayesian Inference
dc.subject.otherAnomaly Monitoring and Diagnosis
dc.subject.otherMultimodal Perception
dc.subject.otherAnomaly Recovery
dc.subject.otherHuman-robot Collaboration
dc.subject.otherRobot Safety and Protection
dc.subject.otherHidden Markov Model
dc.subject.otherRobot Autonomous Manipulation
dc.subject.otheropen access
dc.subject.otherRobotics
dc.subject.otherBayesian inference
dc.subject.otherAutomatic control engineering
dc.subject.otherElectronic devices & materials
dc.subject.otherMachine learning
dc.subject.otherMathematical modelling
dc.subject.otherMaths for engineers
dc.titleNonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
dc.typebook
oapen.identifier.doi10.1007/978-981-15-6263-1
oapen.relation.isPublishedBy6c6992af-b843-4f46-859c-f6e9998e40d5
oapen.imprintSpringer Singapore
oapen.pages137


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