Kim, Byungchan Park, Jooyoung Park, Shinsuk Kang, Sungchul 2024-01-20T19:33:30Z 2024-01-20T19:33:30Z 2021-09-02 2010-04 1083-4419 https://pubs.kist.re.kr/handle/201004/131610 Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC REINFORCEMENT PARAMETERS TORQUE Impedance Learning for Robotic Contact Tasks Using Natural Actor-Critic Algorithm Article 10.1109/TSMCB.2009.2026289 1 IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, v.40, no.2, pp.433 - 443 IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 40 2 433 443 scie scopus 000275665300013 2-s2.0-77949776001 Automation & Control Systems Computer Science, Artificial Intelligence Computer Science, Cybernetics Automation & Control Systems Computer Science Article REINFORCEMENT PARAMETERS TORQUE Contact task equilibrium point control reinforcement learning robot manipulation