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＜電子ブック＞
Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection / by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li

版	1st ed. 2020.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	XVII, 137 p. 50 illus., 44 illus. in color : online resource
著者標目	*Zhou, Xuefeng author Wu, Hongmin author Rojas, Juan author Xu, Zhihao author Li, Shuai author SpringerLink (Online service)
件　名	LCSH:Robotics LCSH:Statistics  LCSH:Control engineering LCSH:Automation LCSH:Machine learning LCSH:Mathematical models FREE:Robotic Engineering FREE:Bayesian Inference FREE:Control, Robotics, Automation FREE:Machine Learning FREE:Mathematical Modeling and Industrial Mathematics
一般注記	Introduction 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 Open Access This 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 HTTP:URL=https://doi.org/10.1007/978-981-15-6263-1

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