Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li.
Zhou, Xuefeng.; Wu, Hongmin.; Rojas, Juan.; Xu, Zhihao.; Li, Shuai, 1983-
2020
TJ211
Available Online

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Title Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li.
Author Zhou, Xuefeng.
ISBN 9789811562631 (electronic book)
9811562636 (electronic book)
9811562628
9789811562624
DOI https://doi.org/10.1007/978-981-15-6263-1
https://doi.org/10.1007/978-981-15-6
Publication Details Singapore : Springer, 2020.
Language English
Description 1 online resource
Item Number 10.1007/978-981-15-6263-1 doi
10.1007/978-981-15-6
Call Number TJ211
Dewey Decimal Classification 629.8/92
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Added Author Wu, Hongmin.
Rojas, Juan.
Xu, Zhihao.
Li, Shuai, 1983-
Available in Other Form Print version: 9789811562624
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction to Robot Introspection
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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.

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