Leveraging Interactional Sociology for Trust Analysis in Multiparty Human-Robot Interaction

By leveraging Interactional Sociology theories, multimodal behavioral features and recurrent neural architectures, we incrementally build computational models for trust analysis in multiparty human-robot interactions (HRI). We show that the model's performance improves when i) modeling group dynamics with different granularities (i.e. group member, dyadic, and group as a whole), and ii) modeling users-robot interactions as a question-answer sequence.