Robot Learning Human Skills and Intelligent Control Design

Description

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... Read more

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.

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Chapter 1 Introduction
1.1 Overview of sEMG-based stiffness transfer
1.2 Overview of robot learning motion skills from humans
1.3 Overview of robot intelligent control design
Chapter 2 Robot platforms and software systems
2.1 Baxter robot
2.2 Nao robot
2.3 KUKA LBR iiwa robot
2.4 Kinect camera
2.5 MYO Armband
2.6 Leap Motion
2.7 Oculus Rift DK 2
2.8 MATLAB Robotics Toolbox
2.9 CoppeliaSim
2.10 Gazebo
Chapter 3 Human-robot stiffness transfer based on sEMG signals
3.1 Introduction
3.2 Brief introduction of sEMG signals
3.3 Calculation of human arm Jacobian matrix
3.4 Stiffness estimation
3.5 Interface design for stiffness transfer
3.6 Human-robot stiffness mapping
3.7 Stiffness transfer for various tasks
3.8 Conclusion

Chapter 4 Learning and Generalisation of Variable Impedance Skills
4.1 Introduction
4.2 Overview of the framework
4.3 Trajectory segmentation
4.4 Trajectory alignment methods
4.5 Dynamical movement primitives
4.6 Modeling of impedance skills
4.7 Experimental study
4.8 Conclusion
Chapter 5 Learning human skills from multimodal demonstration
5.1 Introduction
5.2 System Describtion
5.3 HSMM-GMR Model Description
5.4 Impedance Controller in Task Space
5.5 Experimental Study
5.6 Conclusion

Chapter 6 Skill Modeling based on Extreme Learning Machine
6.1 Introduction
6.2 System of teleoperation-based robotic learning
6.3 Human/robot joint angle calculation using Kinect camera
6.4 Processing of demonstration data
6.5 Skill modeling using extreme learning machine
6.6 Experimental study
6.7 Conclusion

Chapter 7 Neural Network Enhanced Robot Manipulator Control
7.1 Introduction
7.2 Problem description
7.3 Learning from multiple demonstrations
7.4 Neural networks techniques
7.5 Robot manipulator controller design
7.6 Experimental study
7.7 Conclusion
References

Author(s)

Biography

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.

Robot Learning Human Skills and Intelligent Control Design

Description

Table of Contents

Author(s)

Biography

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