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Published on Sep 1, 2015
Authors: Fotios Dimeas Nikos Aspragathos
Robotics Group, Dept. of Mechanical Engineering & Aeronautics University of Patras, Rio 26504, Greece
Abstract: 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