Computational analysis of human-robot interactions through first-person vision: Personality and interaction experience

In this paper, we analyse interactions with Nao, a small humanoid robot, from the viewpoint of human participants through an ego-centric camera placed on their forehead. We focus on human participants´ and robot´s personalities and their impact on the human-robot interactions. We automatically extract nonverbal cues (e.g., head movement) from first-person perspective and explore the relationship of nonverbal cues with participants´ self-reported personality and their interaction experience. We generate two types of behaviours for the robot (i.e., extroverted vs. introverted) and examine how robot´s personality and behaviour affect the findings. Significant correlations are obtained between the extroversion and agreeable-ness traits of the participants and the perceived enjoyment with the extroverted robot. Plausible relationships are also found between the measures of interaction experience and personality and the first-person vision features. We then use computational models to automatically predict the participants´ personality traits from these features. Promising results are achieved for the traits of agreeableness, conscientiousness and extroversion.