Automated Prediction of Extraversion During Human–Humanoid Interaction

Abstract

One of the most important challenges in ro-botics is to achieve natural and engaging interactions between humans and robots. Interpersonal interaction, the process by which two or more people exchange information through verbal and nonverbal messages, could be exploited by robots both to establish interaction and to inform about its quality. The production of social signals being influenced by individual factors, such as personality traits, is critical for robots to have personalized models of interaction. This paper focuses on estimating social traits such as human personality from the dynamics of interpersonal interaction. The work was carried out in the framework of the Project EDHHI (Engagement During Human-Humanoid Interaction) which focuses on understanding individual factors influencing joint human-humanoid actions. In particular, this work addresses the automated prediction of the Extraversion trait during a human-humanoid interaction. We show how it is possible to take into account the specificity of Human-Robot Interaction (HRI) scenarios by contextualizing personality through the attitude that participants show towards the robots. This attitude is influenced by the a priori knowledge that people have on social robotics, and their prior anxiety in interacting with them. The proposed model exploits a set of nonverbal features chosen according to literature in Psychology and Personality Computing. These features are adopted to characterize human behaviors and the dynamics of human-robot interaction. Experimental results highlight that it possible to predict Extraversion of the human partner from nonverbal behavior during human-robot interaction with an accuracy of 62%. A higher accuracy, 70%, is obtained from the computational model by explicitly combining the dynamics of interpersonal interaction and the attitude of the participants towards the robots. Results show that 120s are needed to obtain such performance.

Softness, Warmth, and Responsiveness Improve Robot Hugs

“…This phenomenon is often referred to as "The Chameleon Effect" [4]. Anzalone et al [1] replicated this finding with a robot: they programmed a robot to estimate the level of extraversion of their human partner and adapt its personality/behavior to better match that of the human. The humans perceived the robot to have higher social intelligence when it matched their level of extraversion compared to when the robot did not.…”

Analysis of Personality and EEG Features in Emotion Recognition Using Machine Learning Techniques to Classify Arousal and Valence Labels

“…Robot interaction with people is another vast field of interest for emotion recognition and personality traits. Anzalone and colleagues [41] examined extraversion in human-humanoid interactions using nonverbal behavior (i.e., upper-body movements and interaction duration). Additionally, Bhin and colleagues discussed building an automated psychophysical personality data acquisition system for human-robot interaction under the premise that, to build more natural interaction between human and robots, systems need the ability to recognize the psychological state (i.e., personality) of users [42].…”

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

“…Similarly as the case of personality perception, personality recognition have been addressed in the literature using different data modalities, i.e., still images [50], [96], [97], image sequences [98], [99], audiovisual (with [11], [59], [79], [85], [86], [100], [101], [102], [103] or without [88], [104] interactions) and multimodal [105], [106], [107], [108].…”

“…Multimodal features extracted from depth images (e.g., motion and human-robot distance) are used to train a Logistic Regression Classifier. The work was extended in [106] to consider new features and in depth analysis. Fernadi et al [107] compared different personality recognition methods and investigated the possibility of cross-learning from different social media, i.e.…”