Operator Engagement Detection and Robot Behavior Adaptation in Human-Robot Interaction

It is well known that in human-robot interaction, the effectiveness of a robot varies inversely with the operator engagement in the task. Given the importance of maintaining optimal task engagement when working with a robot, it would be immensely useful to have a robotic system that can detect the level of operator engagement and modify its behavior if required. This paper presents a framework for human-robot interaction in which operator´s physiological signals were analyzed to infer his/her engagement level and the 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 results from affect-elicitation tasks for human participants showed that it is possible to detect engagement through physiological sensing in real-time. A teleoperation-based robotic experiment was also conducted to demonstrate that the presented control architecture allowed the robot to adapt its behavior based on operator engagement level.