The Five Factor Model of Personality

Engagement is crucial to designing intelligent systems that can adapt to the characteristics of their users. This paper focuses on the automatic analysis and classification of engagement based on humans’ and robot’s personality profiles in a triadic human–human–robot interaction setting. More explicitly, we present a study that involves two participants interacting with a humanoid robot, and investigate how participants’ personalities can be used together with the robot’s personality to predict the engagement state of each participant. The fully automatic system is first trained to predict the Big Five personality traits of each participant by extracting individual and interpersonal features from their nonverbal behavioural cues. Second, the output of the personality prediction system is used as an input to the engagement classification system. Third, we focus on the concept of “group engagement”, which we define as the collective engagement of the participants with the robot, and analyze the impact of similar and dissimilar personalities on the engagement classification. Our experimental results show that: 1) using the automatically predicted personality labels for engagement classification yields an F-measure on par with using the manually annotated personality labels, demonstrating the effectiveness of the automatic personality prediction module proposed; 2) using the individual and interpersonal features without utilizing personality information is not sufficient for engagement classification, instead incorporating the participants and robots personalities with individual/interpersonal features increases engagement classification performance; and 3) the best classification performance is achieved when the participants and the robot are extroverted, while the worst results are obtained when all are introverted.

Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

In this paper we introduce a novel dataset, the Multimodal Human-Human-Robot-Interactions (MHHRI) dataset, with the aim of studying personality simultaneously in human-human interactions (HHI) and human-robot interactions (HRI) and its relationship with engagement. Multimodal data was collected during a controlled interaction study where dyadic interactions between two human participants and triadic interactions between two human participants and a robot took place with interactants asking a set of personal questions to each other. Interactions were recorded using two static and two dynamic cameras as well as two biosensors, and meta-data was collected by having participants fill in two types of questionnaires, for assessing their own personality traits and their perceived engagement with their partners (self labels) and for assessing personality traits of the other participants partaking in the study (acquaintance labels). As a proof of concept, we present baseline results for personality and engagement classification. Our results show that (i) trends in personality classification performance remain the same with respect to the self and the acquaintance labels across the HHI and HRI settings; (ii) for extroversion, the acquaintance labels yield better results as compared to the self labels; (iii) in general, multi-modality yields better performance for the classification of personality traits.

/pdf/multimodal-human-human-robot-interactions-mhhri-dataset-for-2f4u6ga0ge.pdf

Multimodal Human-Human-Robot Interactions (MHHRI) Dataset for Studying Personality and Engagement

Estimating the engagement is critical for human - robot interaction. Engagement measures typically rely on the dynamics of the social signals exchanged by the partners, especially speech and gaze. However, the dynamics of these signals is likely to be influenced by individual and social factors, such as personality traits, as it is well documented that they critically influence how two humans interact with each other. Here, we assess the influence of two factors, namely extroversion and negative attitude toward robots, on speech and gaze during a cooperative task, where a human must physically manipulate a robot to assemble an object. We evaluate if the scores of extroversion and negative attitude towards robots co-variate with the duration and frequency of gaze and speech cues. The experiments were carried out with the humanoid robot iCub and N=56 adult participants. We found that the more people are extrovert, the more and longer they tend to talk with the robot; and the more people have a negative attitude towards robots, the less they will look at the robot face and the more they will look at the robot hands where the assembly and the contacts occur. Our results confirm and provide evidence that the engagement models classically used in human-robot interaction should take into account attitudes and personality traits.

Towards engagement models that consider individual factors in HRI: on the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task

Estimating the engagement is critical for human-robot interaction. Engagement measures typically rely on the dynamics of the social signals exchanged by the partners, especially speech and gaze. However, the dynamics of these signals are likely to be influenced by individual and social factors, such as personality traits, as it is well documented that they critically influence how two humans interact with each other. Here, we assess the influence of two factors, namely extroversion and negative attitude toward robots, on speech and gaze during a cooperative task, where a human must physically manipulate a robot to assemble an object. We evaluate if the score of extroversion and negative attitude towards robots co-variate with the duration and frequency of gaze and speech cues. The experiments were carried out with the humanoid robot iCub and N=56 adult participants. We found that the more people are extrovert, the more and longer they tend to talk with the robot; and the more people have a negative attitude towards robots, the less they will look at the robot face and the more they will look at the robot hands where the assembly and the contacts occur. Our results confirm and provide evidence that the engagement models classically used in human-robot interaction should take into account attitudes and personality traits.

Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human–Robot Assembly Task

In this paper we present a system for automatic prediction of extraversion during the first thin slices of human-robot interaction (HRI). This work is based on the hypothesis that personality traits and attitude towards robot appear in the behavioural response of humans during HRI. We propose a set of four non-verbal movement features that characterize human behavior during the interaction. We focus our study on predicting Extraversion using these features extracted from a dataset consisting of 39 healthy adults interacting with the humanoid iCub. Our analysis shows that it is possible to predict to a good level (64 %) the Extraversion of a human from a thin slice of interaction relying only on non-verbal movement features. Our results are comparable to the state-of-the-art obtained in HHI [23].

https://hal.inria.fr/hal-01189065/document

Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

We have investigated the relationship between the excitation frequency and the depth of the flaws in our SQUID based NDE system to achieve optimum frequency for the detection of each particular flaw depth in the metallic samples. Also, the system resolution defined as the gap's distance between the two edges of a crack in the samples, have been experimentally investigated and the dependency to the frequency and the input excitation voltage amplitude was found. Afterwards, we have simulated the system in COMSOL Multiphysics using a basic model for the coils, and examined some special flaws to obtain their effects on the magnetic flux density caused by the eddy current distribution.

Faezeh Rahbar

Papers

Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

Defect-depth dependence of optimal frequency and spatial resolution in SQUID based NDE