Enhancing Multiparty Cooperative Movements: A Robotic Wheelchair that Assists in Predicting Next Actions

Abstract

When an automatic wheelchair or a self-carrying robot moves along with human agents, prediction for the next possible actions by the participating agents, play an important role in realization of successful cooperation among them. In this paper, we mounted a robot to a wheelchair body so that it provides embodied projective signals to the human agents, indicating the next possible action to be performed by the wheelchair. We have analyzed how human participants, particularly when they are in a multiparty interaction, would respond to such a system in experiments. We designed two settings for the robot's projective behavior. The first design allows the robot to face towards the human agents (Face-to-Face model), and the other allows it to face forward as the human agents do, then turn around to the human agents when it indicates where the wheelchair will move to (Body-Torque model). The analysis examined reactions by the human agents to the wheelchair, his/her accompanying human agent, and others who pass by them in the experiment's setting. The results show that the Body-Torque model seems more effective in enhancing cooperative behavior among the human participants than the Face-to-Face model when they are moving to a forward direction together.