Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 47,96
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Edité par Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10 : 9811562652 ISBN 13 : 9789811562655
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 46,39
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierTaschenbuch. Etat : 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.
Edité par Springer Nature Singapore, Springer Nature Singapore Sep 2020, 2020
ISBN 10 : 9811562652 ISBN 13 : 9789811562655
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 42,79
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 60,30
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Vendeur : California Books, Miami, FL, Etats-Unis
EUR 59,51
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Edité par Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10 : 9811562628 ISBN 13 : 9789811562624
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 56,98
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierBuch. Etat : 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.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 62,89
Autre deviseQuantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. pp. XVII, 137 50 illus., 44 illus. in color. 1 Edition NO-PA16APR2015-KAP.
Edité par Springer Nature Singapore, Springer Nature Singapore Jul 2020, 2020
ISBN 10 : 9811562628 ISBN 13 : 9789811562624
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 53,49
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierBuch. Etat : Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 58,67
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Vendeur : California Books, Miami, FL, Etats-Unis
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 60,29
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Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 63,89
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 66,14
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 154 pages. 9.25x6.10x0.44 inches. In Stock.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 47,96
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Vendeur : dsmbooks, Liverpool, Royaume-Uni
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Ajouter au panierPaperback. Etat : New. New. book.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
EUR 98,49
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Ajouter au panierHardcover. Etat : New. New. book.
Edité par Springer Nature Singapore, 2020
ISBN 10 : 9811562652 ISBN 13 : 9789811562655
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 39,60
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Ajouter au panierKartoniert / Broschiert. Etat : 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 context, which can be easily integrated into various robotic systemsIntroduces a fast, accurate, robot anomaly monitoring, diagnosis and&nb.
Edité par Springer Nature Singapore Sep 2020, 2020
ISBN 10 : 9811562652 ISBN 13 : 9789811562655
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 42,79
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierTaschenbuch. Etat : 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.
Edité par Springer Nature Singapore, 2020
ISBN 10 : 9811562628 ISBN 13 : 9789811562624
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 48,37
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Ajouter au panierGebunden. Etat : 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 context, which can be easily integrated into various robotic systemsIntroduces a fast, accurate, robot anomaly monitoring, diagnosis and&nb.
Edité par Springer Nature Singapore Jul 2020, 2020
ISBN 10 : 9811562628 ISBN 13 : 9789811562624
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 53,49
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierBuch. Etat : 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.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 64,46
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Ajouter au panierEtat : New. Print on Demand pp. XVII, 137 50 illus., 44 illus. in color.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 65,88
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. XVII, 137 50 illus., 44 illus. in color.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 82,82
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Ajouter au panierEtat : New. Print on Demand.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 84,15
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Ajouter au panierEtat : New. PRINT ON DEMAND.