본 논문에서는 인간 운동 제어 이론과 기계학습을 기반으로 하여 로봇의 접촉 작업 수행을 위한 새로운 운동 학습 전략을 제시하였다. 성공적인 접촉 작업 수행을 위한 본 연구의 전략은 강화학습 기법을 통하여 최적의 작업 수행을 위한 임피던스 매개 변수를 찾는 것이다. 본 연구에서는 최적의 임피던스 매개 변수를 결정하기 위하여 Recursive Least-Square (RLS) 필터 기반 episodic Natural Actor-Critic 알고리즘이 적용되

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulator's human's superior motor skills in contact tacks.This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG)signals and limb position measurements. The EMG signal is the summation of MUAPs(motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN)model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

In a number of fields, robots are being used for two purposes: efficiency and safety. Most robots, however, have single-actuator mechanism for each joint, where the tasks are performed with high stiffness. High stiffness causes undesired problems to the environment and robots. This study proposes redundant actuator mechanism as an alternative idea to cope with these problems. In this paper, Double-Actuator Unit (DAU) is implemented at each joint for applications of multi-link manipulators. The DAU is composed of two motors: the positioning actuator and the stiffness modulator, which enables independent control of positioning and compliance. A three-link manipulator with DAUs enables adaptive control of RCC. By modulating the joint stiffness of the manipulator and controlling the position of RCC, we can significantly reduce contact force during assembly tasks and surgical procedures.