Predicting User Satisfaction in a Public Space HRI-Scenario

Socially interactive agents (SIA) can be used to assist with information tasks in public spaces like museums or train stations. For the SIAs' learning and adaptability and the SIA operator, it is crucial to assess how users rate their satisfaction with the SIA to establish a foundation for potential modifications. A solution is needed to measure user satisfaction without interrupting robot interactions. One possibility is to gauge and analyze users' affective states. Based on a field test involving N=16 participants, a Furhat robot functioning as a fully autonomous service and information point, dyadic user interactions and satisfaction ratings were utilized to investigate how user affective states can be used to predict user satisfaction. The results indicate that predicting user satisfaction through affective state rating is possible. However, it is important that a significantly larger amount of interaction data, with increased class diversity, is necessary to make a more reliable statement.