A task learning mechanism for the telerobots
Luo, Jing; Yang, Chenguang; Li, Qiang; Wang, Min
Charlie Yang Charlie.Yang@uwe.ac.uk
Professor in Robotics
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|
|Journal||International Journal of Humanoid Robotics|
|Publisher||World Scientific Publishing|
|Peer Reviewed||Peer Reviewed|
|Institution Citation||Luo, J., Yang, C., Li, Q., & Wang, M. (in press). A task learning mechanism for the telerobots. International Journal of Humanoid Robotics, 1950009. https://doi.org/10.1142/S0219843619500099|
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