Coaching robot behavior using continuous physiological affective feedback

Abstract

In this work we present a new way for human-robot interaction, where a robot is able to receive physiological affective feedback for its actions from a human trainer and learn from it. We capture the human trainer's facial expressions using a wearable device that records distal electromyographic signals and uses computational methods of signal processing and pattern recognition in real time. We show how a robot can be coached to perform a certain action when confronted with an object by using the continuous physiological affective feedback from the human trainer. We also show that the robot is able to quickly learn the appropriate actions for different situations from the trainer in a manner modeled after the way children learn from their parent's encouragement or reproach. This work shows an effective way to coach a robot using affective feedback and has the advantage of working in multiple lighting conditions and camera angles as well as not increasing the cognitive load of the trainer. Our method has applications in the area of social robotics because it shows that interaction between humans and robots is possible using continuous non-verbal social cues, which are characteristic for human-human interaction.