By Topic

Learning human control strategy for dynamically stable robots: support vector machine approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yongsheng Ou ; Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China ; Yangsheng Xu

In this paper, we discuss the problem of how human control strategy can be represented as a parametric model using a Support Vector Machine (SVM), and how an SVM-based controller can be used to effectively control a dynamically stable system. We formulate the learning problem as a support vector regression and develop a new SVM learning structure to better implement human control strategy learning in control. The approach is fundamentally valuable in dealing with problems that normally dynamically stable robots experience, such as small sample data and local minima, and therefore is extremely useful in abstracting human controller for dynamic systems. The experimental study on the SVM approach with respect to other approaches clearly demonstrated the superiority of the SVM approach in terms of fidelity, efficiency and effectiveness in implementation.

Published in:

Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on  (Volume:3 )

Date of Conference:

14-19 Sept. 2003