Dynamic grasp recognition within the framework of programming by demonstration

Abstract

Programming robots by unexperienced human users require methods following the Programming by Demonstration (PbD) paradigm. The main goal of these systems is to allow the unexperienced human user to easily integrate motion and perception skills or complex problem solving strategies. Unfortunately actual PbD systems are dealing only with manipulations based on Pick & Place operations. This paper describes how fine manipulations like detecting screw moves can be recognized by a PbD system. Therefore the question: "What happens during a grasp?" has to be answered. In order to do this, finger movements and forces on the fingertips are gathered and analyzed while an object is grasped. This assumes vast sensory employment like a data glove and integrated tactile sensors. An overview of the used tactile sensors and the gathered signals is given. Furthermore a classification of the recognized dynamic grasp is pointed out as well as the classification method based on a support vector machine (SVM).