Eliciting user engagement with a social robot

Abstract

The elicitation of user engagement is one of the current challenges of human-robot interaction, alongside with the identification of appropriate metrics to evaluate users' experience. Several studies focused on strategies aimed at maintaining engagement throughout an interaction, but limited research has been done on how to initiate it by drawing users' attention. The use of social cues in nonverbal human-human communication has been identified as a reliable source of information to determine if a person is engaged. These social cues can be used not only to understand more about human behavior, but also to design robot behaviors that can successfully draw the attention of humans. In this project we look to investigate what are effective techniques to draw attention and elicit initial engagement with a social robot at the entrance of a building. The robot proposed to display these behaviors is the humanoid robot Pepper, from SoftBank Robotics, as it has been specifically designed for human-robot interaction. Initially, the on-board functionalities of the robot are going to be tested. Secondarily, state-of-the-art techniques are going to extend those functionalities to improve Pepper's interactive skills. Eventually, robot behaviors are going to be designed and displayed to participants during an experiment. We aim at understanding the reactions of people to identify the most effective drawing attention behaviors and examine if the encounter with the robot is affected by a novelty effect. Different metrics are proposed to measure both these phenomena in our results. Our system is developed in Python with features extracted from Pepper's Naoqi framework.