Gaze Estimation Learning Architecture as Support to Affective, Social and Cognitive Studies in Natural Human–Robot Interaction

Gaze is a crucial social cue in any interacting scenario and drives many mechanisms of social cognition (joint and shared attention, predicting human intention and coordinating tasks). Gaze is an indication of social and emotional functions affecting the way the emotions are perceived. Evidence shows that embodied humanoid robots endowed with social abilities can be seen as sophisticated stimuli to study several mechanisms of human social cognition while increasing engagement and ecological validity. In this context, building a robotic perception system to automatically estimate the human gaze only relying on robot’s sensors is still demanding. Main goal of the article is to propose a learning robotic architecture estimating the human gaze direction in table-top scenarios without any external hardware. Table-top tasks are largely used in experimental psychology because they are suitable to implement numerous face-to-face collaborative scenarios. Such an architecture can provide a valuable support in studies where external hardware might represent an obstacle to spontaneous human behaviour, especially in environments less controlled than the laboratory (e.g., in clinical settings). A novel dataset was also collected with the humanoid robot iCub, including images annotated from 24 participants in different gaze conditions.