TY  - JOUR
AB  - Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challenging 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.
DA  - 2019
DO  - 10.1142/S0219843619500099
KW  - Telerobotic systems
KW  - Gaussian mixture model
KW  - Gaussian mixture
KW  - regression
KW  - task model
KW  - human-robot interaction
LA  - eng
IS  - 2
PY  - 2019
SN  - 0219-8436
T2  - INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS
TI  - A Task Learning Mechanism for the Telerobots
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29360282
Y2  - 2024-11-22T04:45:12
ER  -