Robot Learning Human Skills and Intelligent Control Design

NOTE EDITORE

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.

SOMMARIO

Chapter 1 Introduction1.1Overview of sEMG-based stiffness transfer1.2Overview of robot learning motion skills from humans1.3Overview of robot intelligent control designChapter 2 Robot platforms and software systems2.1Baxter robot2.2Nao robot2.3KUKA LBR iiwa robot2.4Kinect camera2.5MYO Armband2.6Leap Motion2.7Oculus Rift DK 22.8MATLAB Robotics Toolbox2.9CoppeliaSim2.10GazeboChapter 3 Human-robot stiffness transfer based on sEMG signals3.1Introduction3.2Brief introduction of sEMG signals3.3Calculation of human arm Jacobian matrix3.4Stiffness estimation3.5Interface design for stiffness transfer3.6Human-robot stiffness mapping3.7Stiffness transfer for various tasks3.8Conclusion Chapter 4 Learning and Generalisation of Variable Impedance Skills4.1Introduction4.2Overview of the framework4.3Trajectory segmentation4.4Trajectory alignment methods4.5Dynamical movement primitives4.6Modeling of impedance skills4.7Experimental study4.8ConclusionChapter 5 Learning human skills from multimodal demonstration5.1Introduction5.2System Describtion5.3HSMM-GMR Model Description5.4Impedance Controller in Task Space5.5Experimental Study5.6Conclusion Chapter 6 Skill Modeling based on Extreme Learning Machine6.1Introduction6.2System of teleoperation-based robotic learning6.3Human/robot joint angle calculation using Kinect camera6.4Processing of demonstration data6.5Skill modeling using extreme learning machine6.6Experimental study6.7Conclusion Chapter 7 Neural Network Enhanced Robot Manipulator Control7.1Introduction7.2Problem description7.3Learning from multiple demonstrations7.4Neural networks techniques7.5Robot manipulator controller design7.6Experimental study7.7ConclusionReferences

AUTORE

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, Universität Hamburg. Jianwei Zhang is the director of TAMS, Department of Informatics, Universität Hamburg, Germany.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780367634360
• Dimensioni: 9.25 x 6.25 in Ø 0.09 lb
• Formato: Copertina rigida
• Illustration Notes: 131 b/w images, 9 tables, 45 halftones and 86 line drawings
• Pagine Arabe: 174
• Pagine Romane: xvi