9789811562655 - Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection by Zhou, Xuefeng; Wu, Hongmin; Rojas, Juan; Xu, Zhihao; Li, Shuai (12 results)

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Condition: New. pp. XVII, 137 50 illus., 44 illus. in color. 1st ed. 2020 edition NO-PA16APR2015-KAP.

Condition: New.

Condition: New.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book focuses onrobot introspection,whichhas a direct impact on physical human-robot interactionandlong-term autonomy,andwhich can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,the abilitytoreason,solve their ownanomaliesand proactivelyenrich 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 caneffectivelybe modeled as a parametrichidden Markovmodel (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using thehierarchical Dirichletprocess (HDP) on the standard HMM parameters,known as theHierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states andallows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is avaluablereferenceresource forresearchers and designers inthe fieldof robot learning and multimodal perception, as well as for senior undergraduate and graduateuniversitystudents.

Taschenbuch. Condition: Neu. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection | Xuefeng Zhou (u. a.) | Taschenbuch | xvii | Englisch | 2020 | Springer | EAN 9789811562655 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condition: new. Questo è un articolo print on demand.

Condition: New. Print on Demand pp. XVII, 137 50 illus., 44 illus. in color.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access book focuses onrobot introspection,whichhas a direct impact on physical human-robot interactionandlong-term autonomy,andwhich can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,the abilitytoreason,solve their ownanomaliesand proactivelyenrich 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 caneffectivelybe modeled as a parametrichidden Markovmodel (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using thehierarchical Dirichletprocess (HDP) on the standard HMM parameters,known as theHierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states andallows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is avaluablereferenceresource forresearchers and designers inthe fieldof robot learning and multimodal perception, as well as for senior undergraduate and graduateuniversitystudents. 156 pp. Englisch.

Condition: New. PRINT ON DEMAND pp. XVII, 137 50 illus., 44 illus. in color.

Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Is the first book on robot introspection based on nonparametric Bayesian methods in a data-driven&nbspcontext, which can be easily integrated into various robotic systemsIntroduces a fast, accurate, robot anomaly monitoring, diagnosis&nbspand&nb.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 156 pp. Englisch.