Reinforcement Learning of Variable Admittance Control for Human-Robot Co-manipulation [IROS 2015 ]




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Published on Sep 1, 2015

Fotios Dimeas
Nikos Aspragathos

Robotics Group, Dept. of Mechanical Engineering & Aeronautics
University of Patras, Rio 26504, Greece

In this paper, a variable admittance controller based on reinforcement learning is proposed for human-robot co-manipulation tasks. Setting as the goal of the reinforcement learning algorithm the minimisation of the jerk throughout a point-to-point movement, the proposed controller can learn the appropriate damping for effective cooperation without any prior knowledge of the target position or other task characteristics. The performance of the proposed variable admittance controller is investigated on a co-manipulation task with a number of subjects using a KUKA LWR robot, demonstrating considerable reduction both in the effort required by the operator and in the completion time of the task.

Paper appears in: IROS 2015
The 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems will take place in Hamburg, Germany, September 28 - October 02, 2015


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