Iterative Learning of Variable Impedance Control for Human-Robot Cooperation

In this study, we propose a novel iterative learning scheme, which generates time-series data comprising the impedance value for human-robot cooperative work, where the human operator moves the end-effector from an initial position to a goal position. The proposed learning scheme iteratively updates time-series data comprising the variable impedance value to minimize a task-oriented cost function. Two types of noise reduction technique are used in the proposed learning scheme, which reduce the noise in the time direction and in the trial direction. The validity of the proposed method was verified based on experiments.