A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Learning soft task priorities for control of redundant robots
2016
2016 IEEE International Conference on Robotics and Automation (ICRA)
One of the key problems in planning and control of redundant robots is the fast generation of controls when multiple tasks and constraints need to be satisfied. In the literature, this problem is classically solved by multi-task prioritized approaches, where the priority of each task is determined by a weight function, describing the task strict/soft priority. In this paper, we propose to leverage machine learning techniques to learn the temporal profiles of the task priorities, represented as
doi:10.1109/icra.2016.7487137
dblp:conf/icra/ModugnoNRO0I16
fatcat:vjfipq6hizdvvl4umdusc4gz3e