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.

About the Author

Chenguang Yang is a Co-Chair of the Technical Committee on Collaborative Automation for Flexible Manufacturing (CAFM), IEEE Robotics and Automation Society and Co-Chair of the Technical Committee on Bio-mechatronics and Bio-robotics Systems (B2S), IEEE Systems, Man, and Cybernetics Society.

Chao Zeng is currently a Research Associate at the Institute of Technical Aspects of Multimodal Systems, Universit�t Hamburg.

Jianwei Zhang is the director of TAMS, Department of Informatics, Universit�t Hamburg, Germany.

"About this title" may belong to another edition of this title.

Store Description

Visit Seller's Storefront

Seller's business information

Edward Bowditch Ltd
Exstowe, Exton, Exeter, EX3 0PP, United Kingdom

Sale & Shipping Terms

Terms of Sale

Legal entity name: Edward Bowditch Ltd
Legal entity form: Limited company
Business correspondence address: Exstowe, Exton, Exeter, EX3 0PP
Company registration number: 04916632
VAT registration: GB834241546
Authorised representative: Mr. E. Bowditch

Shipping Terms

Orders usually dispatched within two working days. Please note that at this time all domestic United Kingdom orders are sent by trackable UPS courier, we choose not to offer a lower cost alternative.

Shipping rates from United Kingdom to U.S.A.

Shipping rates from United Kingdom to U.S.A.
Order quantity	7 to 14 business days	2 to 3 business days
First item	US$ 13.31	US$ 19.96

Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.