Extraction of Robotic Surface Processing Strategies from Human Demonstrations

Abstract

Learning from Demonstration (LfD) is a widely used approach for teaching robot motion, but more sophisticated strategies are required to address complex tasks such as surface processing. Sanding is an example where comprehensive strategies are necessary to ensure complete and efficient coverage of the surface of a workpiece. In this paper, we present a system that captures human motions and contact forces during surface processing using a powered sanding tool. We provide a publicly available dataset that consists of demonstrations for various geometric shapes with the goal to extract robot execution strategies through LfD from a variety of users. This is in contrast to conventional LfD, which generates a policy directly from one or multiple trajectories provided by a single user. Further, we provide a data analysis that reveals key insights into how humans adapt their strategies to different surface geometries and extract robot execution strategies from it. Finally, we conduct two basic robotic experiments justifying the approach of strategy extraction. Our findings contribute to the understanding of human surface-processing behavior and lay the foundation for developing more effective robotic surface processing strategies.