Dynamic Grasp Recognition within the Framework of Programming by Demonstration

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 ne 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, nger movements and forces on the ngertips are gathered and analyzed while a object is grasped. This assume 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 classi cation of the recognized Dynamic Grasp is pointed out as well as the classi cation method based on a Support Vector Machine (SVM).