Controlling the Impression of Robots via GAN-based Gesture Generation

As a type of body language, gestures can largely affect the impressions of human-like robots perceived by users. Recent data-driven approaches to the generation of co-speech gestures have successfully promoted the naturalness of produced gestures. These approaches also possess greater generalizability to work under various contexts than rule-based methods. However, most have no direct control over the human impressions of robots. The main obstacle is that creating a dataset that covers various impression labels is not trivial. In this study, based on previous findings in cognitive science on robot impressions, we present a heuristic method to control them without manual labeling, and demonstrate its effectiveness on a virtual agent and partially on a humanoid robot through subjective experiments with 50 participants.