Adaptive Interaction of Persistent Robots to User Temporal Preferences

We look at the problem of enabling a mobile service robot to autonomously adapt to user preferences over repeated interactions in a long-term time frame, where the user provides feedback on every interaction in the form of a rating. We assume that the robot has a discrete and finite set of interaction options from which it has to choose one at every encounter with a given user. We first present three models of users which span the spectrum of possible preference profiles and their dynamics, incorporating aspects such as boredom and taste for change or surprise. Second, given the model to which the user belongs to, we present a learning algorithm which is able to successfully learn the model parameters. We show the applicability of our framework to personalizing light animations on our mobile service robot, CoBot.