Identification of Common Force-based Robot Skills from the Human and Robot Perspective

Abstract

Learning from Demonstration (LfD) can significantly speed up the knowledge transfer from human to robot, which has been proven for relatively unconstrained actions such as pick and place. However, transferring contact or force-based skills (contact skills) to a robot is noticeably harder since force and position constraints need to be considered simultaneously. We propose a set of contact skills, which differ in the force and kinematic constraints. In a first user study, several subjects were asked to term a variety of force-based interactions, from which skill names were derived. In a second and third user study, the identified skill names are used to let a test group of subjects classify the shown interactions. To evaluate the skill recognition from the robot perspective, we propose a feature-based classification scheme to recognize such skills with a robotic system in a LfD setting. Our findings prove that humans are able to understand the meaning of the different skills and, using the classification pipeline, the robot is able to recognize the different skills from human demonstrations.