International audienceTo investigate the dynamics of human-robot acceptance, we carried out an experimental study with 56 adult participants and the iCub robot. Trust in the robot has been considered as a main indicator of acceptance and measured by the participants' conformation to the iCub's answers to questions on functional and social tasks characterized by perceptual and socio cognitive uncertainty. In particular, we were interested in understanding whether (i) trust in functional savvy is a prerequisite for trust in social savvy, and (ii) to what extent factors such as participants' desire for control, attitude towards social influence of robots, and imagined collaborative vs. competitive scenario, may influence their trust in the iCub. We found that participants conformed more to the iCub's answers in the functional than in the social tasks. Moreover, the few participants conforming to the iCub's answers in the social task also conformed less in the functional issues. Trust in the robot's functional savvy does not thus seem to be a prerequisite for trust in its social savvy. Finally, the examined factors did not influence the trust in iCub. Results are discussed with relation to methodology of human-robot interaction (HRI) research

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 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.

A new race of artifacts comes equipped with behavioral properties. Those properties transmute the very nature of the object, granting it a life of its own and a special status that stems from the psychological attributions humans naturally produce when confronted by autonomous movements. This article examines what makes behavioral objects special in terms of the psychological properties they evoke in an observer. We look into the notion of behavior and evaluate to what extent the concept of anthropomorphism is a valid construct when considering the behavior of artificial objects. Based on recent research in cognitive psychology, we propose a framework to conceptualize the way people infer psychological attributes from movement, and the way it applies to behavioral objects.

No abstract

No abstract

No abstract

Abstract. 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 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].

No abstract