Progressive learning for robotic assembly: learning impedance with an excitation scheduling method

Abstract

A novel approach to stable learning control is developed inspired by human learning behavior, and applied to an impedance learning problem for high-speed dynamic robotic assembly. The new method termed "progressive learning" uses scheduled excitation inputs that allow the system to learn quasi-static, slow modes in the beginning, followed by the learning of faster modes. This new method is presented in the context of high speed robotic assembly, where an impedance control law is learned with this excitation scheduling method. Extensive simulation results are provided to demonstrate the effectiveness of this method. A detailed analysis of the mechanism of progressive learning is also provided and verified through simulation.