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

Abstract

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.