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    Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

    Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

    Xuefeng ZhouHongmin WuJuan RojasZhihao XuShuai Li

    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.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.

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    description_of_book

    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

    additional_information

    vendor

    publication

    publication_date

    2020 Aug 15

    authors
    Xuefeng ZhouHongmin WuJuan RojasZhihao XuShuai Li

    isbn

    978-981-15-6263-1

    about_the_authors

    Xuefeng Zhou
    Xuefeng Zhou

    Robotic Team Guangdong Institute of Intelligent Manufacturing Guangzhou, Guangdong, China

    Xuefeng Zhou
    Hongmin Wu
    Hongmin Wu

    Robotic Team Guangdong Institute of Intelligent Manufacturing Guangzhou, Guangdong, China

    Hongmin Wu
    Juan Rojas
    Juan Rojas

    School of Electromechanical Engineering Guangdong University of Technology Guangzhou, China.

    Juan Rojas
    Zhihao Xu

    Robotic Team Guangdong Institute of Intelligent Manufacturing Guangzhou, Guangdong, China

    Zhihao Xu
    Shuai Li

    School of Engineering Swansea University Swansea, UK

    Shuai Li

    tags

    Collaborative Robot IntrospectionNonparametric Bayesian InferenceAnomaly Monitoring and DiagnosisMultimodal PerceptionAnomaly RecoveryHuman-robot CollaborationRobot Safety and ProtectionHidden Markov ModelRobot Autonomous Manipulationopen access

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