A New Approach to Implicit Human-Robot Interaction Using Affective Cues

Abstract

It is well known that in social interactions, implicit communication between the communicators plays a significant role. It would be immensely useful to have a robotic system that is capable of such implicit communication with the operator and can modify its behavior if required. This paper presents a framework for human-robot interaction in which the operator's physiological signals were analyzed to infer his/her probable anxiety level and robot behavior was adapted as a function of the operator affective state. Peripheral physiological signals were measured through wearable biofeedback sensors and a control architecture inspired by Riley's original information-flow model was developed to implement such human-robot interaction. The target affective state chosen in this work was anxiety. The results from affect-elicitation tasks for human participants showed that it is possible to detect anxiety through physiological sensing in real-time. A robotic experiment was also conducted to demonstrate that the presented control architecture allowed the robot to adapt its behavior based on operator anxiety level.