Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach (Studies in Systems, Decision and Control)

Li, Shuai

Zhou, Xuefeng

Zhang, Yinyan

Reference

Springer

<p><div>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.&nbsp;</div> <div>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.</div></p>

AI based Robot Safe Learning and Control

Computers & Technology

Xu, Zhihao

Wu, Hongmin

Cheng, Taobo

Lv, Xiaojing

Computer Science

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

<p><p>This open access book focuses on&nbsp;robot introspection,&nbsp;which&nbsp;has a direct impact on physical human-robot interaction&nbsp;and&nbsp;long-term autonomy,&nbsp;and&nbsp;which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,&nbsp;the ability&nbsp;to&nbsp;reason,&nbsp;solve their own&nbsp;anomalies&nbsp;and proactively&nbsp;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&nbsp;effectively&nbsp;be modeled as a parametric&nbsp;hidden Markov&nbsp;model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the&nbsp;hierarchical Dirichlet&nbsp;process (HDP) on the standard HMM parameters,&nbsp;known as the&nbsp;Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and&nbsp;allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.</p> <p>This book is a&nbsp;valuable&nbsp;reference&nbsp;resource for&nbsp;researchers and designers in&nbsp;the field&nbsp;of robot learning and multimodal perception, as well as for senior undergraduate and graduate&nbsp;university&nbsp;students.</p></p>

Rojas, Juan

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