Robot impedance generation from logic task description through progressive learning

Abstract

In this paper, we present a new approach to learning robot impedance control parameters from a logic task description. In this approach, we first describe the desired behaviour of a robot for performing a given task at a logic level. A simple logic branch control using a quasi-static force-to-motion map is created based on the logic description. The progressive learning method is then applied to this logic branch control in order to create a dynamic control, i.e. impedance control, for performing the task quickly and dynamically. Starting with a simple logic description about the robot behaviour, the system can develop a fully dynamic impedance control by progressively learning the process dynamics. The problem is formulated in the context of high-speed insertion, and the proposed approach is verified through simulation.