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Robot learning human skills and intelligent control design

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robot learning human skills and intelligent control design/ Chenguang Yang, Chao Zeng, Jianwei Zhang.
作者:	Yang, Chenguang.
其他作者:	Zeng, Chao.
出版者:	Boca Raton, FL :CRC Press, : 2021.,
面頁冊數:	1 online resource.
標題:	Human-robot interaction. -
電子資源:	https://www.taylorfrancis.com/books/9781003119173
ISBN:	9781003119173

Robot learning human skills and intelligent control design
Yang, Chenguang.

Robot learning human skills and intelligent control design[electronic resource] /Chenguang Yang, Chao Zeng, Jianwei Zhang. - 1st ed. - Boca Raton, FL :CRC Press,2021. - 1 online resource.

Includes bibliographical references and index.

In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user's arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.

ISBN: 9781003119173Subjects--Topical Terms:

1612845
Human-robot interaction.

LC Class. No.: TJ211.49

Dewey Class. No.: 629.8924019

Robot learning human skills and intelligent control design
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245 1 0 $a Robot learning human skills and intelligent control design $h [electronic resource] / $c Chenguang Yang, Chao Zeng, Jianwei Zhang.
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300 $a 1 online resource.
504 $a Includes bibliographical references and index.
520 $a In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user's arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.
588 $a Description based on print version record.
650 0 $a Human-robot interaction. $3 1612845
650 0 $a Human engineering. $3 522813
650 0 $a Machine learning. $3 533906
650 0 $a Intelligent control systems. $3 546464
700 1 $a Zeng, Chao. $3 3341191
700 1 $a Zhang, Jianwei. $3 1912993
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