Analyzing human high-fives to create an effective high-fiving robot

Abstract

Creating a robot that can teach humans simple interactive tasks such as high-fiving requires research at the intersection of physical human-robot interaction (PHRI) and socially assistive robotics. This paper shows how observation of natural human-human interaction can improve the design of requirements for social-physical robots and form a framework for autonomous execution of interactive physical tasks. Eleven pairs of human subjects were recruited to perform a set of high-fiving games; a magnetic motion tracker and an accelerometer were mounted to each person's hand for the duration of the experiment, and each subject completed several questionnaires about the experience. The results reveal valuable clues about the generally positive feelings of the participants and the movement of their hands during play. We discuss how we plan to use these results to create a robot that can teach humans similar high-fiving games.