Operational “Feel” Adjustment by Reinforcement Learning for a Power-Assisted Positioning Task
Tetsuya Morizono*, Yoji Yamada**, and Masatake Higashi***
*Department of Information and Systems Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, Japan
**Department of Mechanical Science and Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
***Department of Advanced Science and Technology, Toyota Technological Institute, 2-12-1 Hisakata, Tempaku-ku, Nagoya 468-8511, Japan
Controlling “feel” when operating a power-assist robot is important for improving robot operability, user satisfaction, and task performance efficiency. Autonomous adjustment of “feel” is considered with robots under impedance control, and reinforcement learning in adjustment when a task includes repetitive positioning is discussed. Experimental results demonstrate that an operational “feel” pattern appropriate for positioning at a goal is developed by adjustment. Adjustment assuming a single fixed goal is expanded to cases including multiple goals, in which it is assumed that one goal is chosen by a user in real time. To adjust operational “feel” to individual goals, an algorithm infers the goal. The same result as that for a single fixed goal is obtained in experiments, but experimental results suggest that design must be improved to where the accuracy of inference to the goal is taken into account by the adjustment learning algorithm.
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