Multimodal Episodic Analysis of Human Personality Traits for Personalized Behavior in Social Robots

This paper presents a longitudinal analysis of hu-man personality for socially interactive robots. A combination of visual, verbal, and paralinguistic cues has been employed to predict human personality and generate a robot's personalized behavior in an episodic manner. Low-level perceptual features have been extracted with a stereo vision camera sensor and a wireless microphone. A higher-level interpretation of these features provides us with suitable cues for personality assessment. A Random Forest supervised learning algorithm has been applied to classify the personality label of each individual interacting with the social robot, Emah. Three personality traits: Extraversion-Introversion, Agreeableness-Self centered, and Openness-Traditionalist are considered in this paper. We utilized the long-term memory of the interactive robot to enhance the likelihood of personality estimation. Suitable per-sonalized robot behavior has been generated in the form of speech, gestures, and facial expressions. The proposed system has been validated in real-world human-robot interaction sce-narios. The evaluation results show promising improvement in the adaptability of the robot's behavior, provided multimodal as well as episodic personality assessment is employed.