Templates and Graph Neural Networks for Social Robots Interacting in Small Groups of Varying Sizes

Abstract

Social robots need to be able to interact effectively with small groups. While there is a significant interest in human-robot interaction in groups, little focus has been placed on developing autonomous social robot decision-making methods that operate smoothly with small groups of any size (e.g. 2, 3, or 4 interactants). In this work, we propose a Template- and Graph-based Modeling approach for robots interacting in small groups (TGM), enabling them to interact with groups in a way that is group-size agnostic. Critically, we separate the decision about the target of their communication, or “whom to address?” from the decision of “what to communicate?”, which allows us to use template-based actions. We further use Graph Neural Networks (GNNs) to efficiently decide on “whom“ and “what”. We evaluated TGM using imitation learning and compared the structured reasoning achieved through GNNs to unstructured approaches for this two-part decision-making problem. On two different datasets, we show that TGM outperforms the baselines encouraging future work to invest in collecting larger datasets.