Variable Impedance Control with Simplex Gradient based Iterative Learning for Human-Robot Collaboration

Abstract

In this paper, a variable impedance control method with simplex gradient based iterative learning is proposed to improve the performance of human-robot collaboration. First, continuous impedance parameters are represented as a function of time by using several Gaussian functions. Parameters of these Gaussian functions are then iteratively updated to minimize the human operator’s physical effort by using simplex gradients, which are gradients approximated from past data. Finally, experiments with a 2-DOF planar robot arm in a collaborative reaching task are performed to verify the proposed method.