Comparing Models of Disengagement in Individual and Group Interactions

Changes in type of interaction (e.g., individual vs. group interactions) can potentially impact data-driven models developed for social robots. In this paper, we provide a first investigation in the effects of changing group size in datadriven models for HRI, by analyzing how a model trained on data collected from participants interacting individually performs in test data collected from group interactions, and \textit{vice-versa. Another model combining data from both individual and group interactions is also investigated. We perform these experimentsin the context of predicting disengagement behaviors in children interacting with two social robots. Our results show that a model trained with group data generalizes better to individual participants than the other way around. The mixed model seems a good compromise, but it does not achieve the performance levels of the models trained for a specific type of interaction.