Mood Estimation as a Social Profile Predictor in an Autonomous, Multi-Session, Emotional Support Robot for Children

In this work, we created an end-to-end autonomous robotic platform to give emotional support to children in long-term, multi-session interactions. Using a mood estimation algorithm based on visual cues of the user’s behaviors through their facial expressions and body posture, a multidimensional model predicts a qualitative measure of the subject’s affective state. Using a novel Interactive Reinforcement Learning algorithm, the robot is able to learn over several sessions the social profile of the user, adjusting its behavior to match their preferences. Although the robot is completely autonomous, a third party can optionally provide feedback to the robot through an additional UI to accelerate its learning of the user’s preferences. To validate the proposed methodology, we evaluated the impact of the robot on elementary school aged children in a long-term, multi-session interaction setting. Our findings show that using this methodology, the robot is able to learn the social profile of the users over a number of sessions, either with or without external feedback as well as maintain the user in a positive mood, as shown by the consistently positive rewards received by the robot using our proposed learning algorithm.