Hongmin Wu's Books

AI based Robot Safe Learning and Control

By: Xuefeng Zhou,Zhihao Xu,Shuai Li,Hongmin Wu,Taobo Cheng,Xiaojing Lv

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AI based Robot Safe Learning and Control

By: Xuefeng Zhou,Zhihao Xu,Shuai Li,Hongmin Wu,Taobo Cheng,Xiaojing Lv

This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

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Page Count

138

Publisher

Springer Nature

Published Date

ISBN 10

9811555036

ISBN 13

9789811555039

AI based Robot Safe Learning and Control

By: Xuefeng Zhou,Zhihao Xu,Shuai Li,Hongmin Wu,Taobo Cheng,Xiaojing Lv

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AI based Robot Safe Learning and Control

By: Xuefeng Zhou,Zhihao Xu,Shuai Li,Hongmin Wu,Taobo Cheng,Xiaojing Lv

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

138

Publisher

Springer Nature

Published Date

ISBN 10

9811555036

ISBN 13

9789811555039

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

By: Xuefeng Zhou,Hongmin Wu,Juan Rojas,Zhihao Xu,Shuai Li

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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

By: Xuefeng Zhou,Hongmin Wu,Juan Rojas,Zhihao Xu,Shuai 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.

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Page Count

137

Publisher

Springer

Published Date

ISBN 10

9811562652

ISBN 13

9789811562655

Book's by Hongmin Wu

AI based Robot Safe Learning and Control

AI based Robot Safe Learning and Control

AI based Robot Safe Learning and Control

AI based Robot Safe Learning and Control

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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