Learning force sensory patterns and skills front human demonstration

Abstract

The motivation behind this work is to transfer force-based assembly skills to robots by using human demonstration. For this purpose, we model the skills as a sequence of contact formations (which describe how a workpiece touches its environment) and desired transitions between contact formations. In this paper, we present a method of identifying single-ended contact formations from force sensor patterns. Instead of using geometric models of the workpieces, fuzzy logic is used to learn and model the patterns in the force signals. Membership functions are generated automatically from training data and then used by the fuzzy classifier. This classification scheme is used to learn desired sequences of contact formations which comprise a force-based skill. Experimental results are presented which use the technique to extract skill information from human demonstration data.