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A task learning mechanism for the telerobots

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

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Authors

Jing Luo

Qiang Li

Min Wang



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.

Citation

Luo, J., Yang, C., Li, Q., & Wang, M. (2019). A task learning mechanism for the telerobots. International Journal of Humanoid Robotics, 16(2), 1950009. https://doi.org/10.1142/S0219843619500099

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
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

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