2022/08/24 by Di Fu, Fu, Di, Fares Abawi +5
Computer Science · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Robotics (cs.RO) #cs.HC #cs.RO
paper · pdf · doi:10.48550/arxiv.2208.11647
2 pages, 1 figure, accepted by RO-MAN 2022 Workshop on Machine Learning for HRI: Bridging the Gap between Action and Perception
arxiv created 2022/08/25 · arxiv updated 2022/08/26
Human eye gaze plays an important role in delivering information, communicating intent, and understanding others' mental states. Previous research shows that a robot's gaze can also affect humans' decision-making and strategy during an interaction. However, limited studies have trained humanoid robots on gaze-based data in human-robot interaction scenarios. Considering gaze impacts the naturalness of social exchanges and alters the decision process of an observer, it should be regarded as a crucial component in human-robot interaction. To investigate the impact of robot gaze on humans, we propose an embodied neural model for performing human-like gaze shifts. This is achieved by extending a social attention model and training it on eye-tracking data, collected by watching humans playing a game. We will compare human behavioral performances in the presence of a robot adopting different gaze strategies in a human-human cooperation game.