Choosing Robot Feedback Style to Optimize Human Exercise Performance

Different people respond to feedback and guidance in different ways, and their preferences may change based on their mood, tiredness, etc. We present a robot exercise coach that provides verbal and nonverbal feedback in two different styles: firm and encouraging. We collect a dataset of people experiencing both feedback styles and show that the style that someone performs best with may not be the one they have the best subjective experience with or be the one that they state they prefer. To account for this, we present a contextual bandit approach that enables the robot coach to learn the best style to use over time to improve the human's performance, and show that this approach performs quite well in expectation on the real human data.