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.