A task learning mechanism for the telerobots

Luo, Jing; Yang, Chenguang; Li, Qiang; Wang, Min

Abstract

Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challengeable to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot.

Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method.

• Journal Article Type: Article
• Acceptance Date: Mar 25, 2019
• Online Publication Date: May 9, 2019
• Publication Date: Apr 1, 2019
• Deposit Date: May 15, 2019
• Publicly Available Date: May 15, 2019
• Journal: International Journal of Humanoid Robotics
• Print ISSN: 0219-8436
• Electronic ISSN: 1793-6942
• Publisher: World Scientific Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 2
• Pages: 1950009
• DOI: https://doi.org/10.1142/S0219843619500099
• Public URL: https://uwe-repository.worktribe.com/output/847183
• Publisher URL: http://doi.org/10.1142/S0219843619500099
• Contract Date: May 15, 2019