The influence of individual social traits on robot learning in a human-robot interaction

Interactive Machine Learning considers that a robot is learning with and/or from a human. In this paper, we investigate the impact of human social traits on the robot learning. We explore social traits such as age (children vs. adult) and pathology (typical developing children vs. children with autistic spectrum disorders). In particular, we consider learning to recognize both postures and identity of a human partner. A human-robot posture imitation learning, based on a neural network architecture, is used to develop a multi-task learning framework. This architecture exploits three learning levels : 1) visual feature representation, 2) posture classification and 3) human partner identification. During the experiment the robot interacts with children with autism spectrum disorders (ASD), typical developing children (TD) and healthy adults. Previous works assessed the impact on learning of these social traits at the group level. In this paper, we focus on the analysis of individuals separately. The results show that the robot is impacted by the social traits of these different groups' individuals. First, the architecture needs to learn more visual features when interacting with a child with ASD (compared to a TD child) or with a TD child (compared to an adult). However, this surplus in the number of neurons helped the robot to improve the TD children's posture recognition but not that of children with ASD. Second, preliminary results show that this need of a neurons surplus while interacting with children with ASD is also generalizable to the identity recognition task.