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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection - Couverture rigide
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Dr. Xuefeng Zhou is an Associate Professor and Leader of the Robotics Team at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Manufacturing and Automation from the South China University of Technology in 2011. His research mainly focuses on motion planning and control, force control and legged robots. He has published more than 40 journal articles and conference papers.
Dr. Hongmin Wu is a Researcher at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Mechanical Engineering from Guangdong University of Technology, Guangzhou, China, in 2019. His research mainly focuses on robot learning, autonomous manipulation, deep learning and human--robot collaboration. He has published more than 20 journal articles and conference papers.Dr. Juan Rojas is an "100 Young Talents" Associate Professor at the Guangdong University of Technology inGuangzhou, China, where he works at the Biomimetics and Intelligent Robotics Lab (BIRL). Dr. Rojas currently researches robot introspection, human intention prediction, high-level state estimation and skill acquisition for manipulation tasks. He has published more than 40 journal articles and conference papers. Dr. Rojas serves as an Associate Editor of Advanced Robotic Journal since 2019 and Associate Editor of IEEE International Conference on Intelligent Robots and Systems (IROS) since 2017.Dr. Zhihao Xu is a Researcher at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Control Science and Engineering from Nanjing University of Science and Technology, China, in 2016. His research mainly focuses on intelligent control theory, motion planning and control and force control. He has published more than 30 journal articles and conference papers.
Prof. Shuai Li is a Ph.D. Supervisor and Associate Professor (Reader) at the College of Engineering, Swansea University, UK. He received his Ph.D. degree in Electrical and Computer Engineering from Stevens Institute of Technology, New Jersey, USA, in 2014. His research interests are robot manipulation, automation and instrumentation, artificial intelligence and industrial robots. He has published over 80 papers in journals such as IEEE TAC, TII, TCYB, TIE and TNNLS. He serves as Editor-in-Chief of the International Journal of Robotics and Control and was the General Co-Chair of the 2018 International Conference on Advanced Robotics and Intelligent Control.Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurSpringer Verlag, Singapore
- Date d'édition2020
- ISBN 10 9811562628
- ISBN 13 9789811562624
- ReliureRelié
- Langueanglais
- Nombre de pages137
- Coordonnées du fabricantnon disponible
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Edité par
Springer Nature Singapore, 2020
ISBN 10 : 9811562628
ISBN 13 : 9789811562624
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Gebunden. 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. N° de réf. du vendeur 373577484
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Edité par
Springer Nature Singapore Jul 2020, 2020
ISBN 10 : 9811562628
ISBN 13 : 9789811562624
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. 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. N° de réf. du vendeur 9789811562624
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Edité par
Springer Nature Singapore, Springer Nature Singapore Jul 2020, 2020
ISBN 10 : 9811562628
ISBN 13 : 9789811562624
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. 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. N° de réf. du vendeur 9789811562624
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Edité par
Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10 : 9811562628
ISBN 13 : 9789811562624
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. 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. N° de réf. du vendeur 9789811562624
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Edité par
Springer, 2020
ISBN 10 : 9811562628
ISBN 13 : 9789811562624
Ancien ou d'occasion
Couverture rigide
Vendeur : Big River Books, Powder Springs, GA, Etats-Unis
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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