Incremental Policy Refinement by Recursive Regression and Kinesthetic Guidance

Abstract

Fast deployment of robot tasks requires appropriate tools that enable efficient reuse of existing robot control policies. Learning from Demonstration (LfD) is a popular tool for the intuitive generation of robot policies, but the issue of how to address the adaptation of existing policies has not been properly addressed yet. In this work, we propose an incremental LfD framework that efficiently solves the above-mentioned issue. It has been implemented and tested on a number of popular collaborative robots, including Franka Emika Panda, Universal Robot UR10, and KUKA LWR 4.