A Preference Learning System for the Autonomous Selection and Personalization of Entertainment Activities during Human-Robot Interaction

Social robots assisting in cognitive stimulation therapies, physical rehabilitation, or entertainment sessions have gained visibility in the last years. In these activities, users may present different features and needs, so personalization is essential. This manuscript presents a Preference Learning System for social robots to personalize Human-Robot Interaction during entertainment activities. Our system is integrated into Mini, a social robot dedicated to research with a wide repertoire of entertainment activities like games, displaying multimedia content, or storytelling. The learning model we propose consists of four stages. First, the robot creates a unique profile of its users by obtaining their defining features using interaction. Secondly, a Preference Learning algorithm predicts the users’ favorite entertainment activities using their features and a database with the features and preferences of other users. Third, the prediction is adapted using Reinforcement Learning while entertainment sessions occur. Finally, the robot personalizes Human-Robot Interaction by autonomously selecting the users’ favorite activities. Thus, the robot aims at promoting longer-lasting interactions and sustaining engagement.