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International Journal of Social Robotics
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Comfortability Analysis Under a Human–Robot Interaction Perspective

Authors: Redondo Lechuga M. E.; Niewiadomski R.; Rea F.; Incao S.; Sandini G.; Sciutti A.;

Comfortability Analysis Under a Human–Robot Interaction Perspective

Abstract

AbstractInteractions entail a tangled mix of emotional states that emerge between the people who are communicating. Being capable of comprehending these states help us to adapt to our partner’s needs enhancing the interaction. In the same fashion, we believe that robots capable of such skills would be better integrated in society. Hence, this paper tackles the internal state that focuses on the unfolding of any social exchange:Comfortability. It explores whether a humanoid robot can have an impact on humansComfortabilityand explores the way people might behave. To this aim, the iCub robot interviewed 29 non-HRI researchers for a real column of the IIT Opentalk online magazine. During the interview the robot complimented, ignored, interrupted, and insulted the participant with the intention of making them feel oppositeComfortabilitylevels. The results concluded that a humanoid robot can affect people’sComfortabilityhighlighting that not everyone perceives, assimilates, and reacts to the same situations in the same way. The findings suggest that self-reports andValence/Arousalestimations are not reliable measures to determine someone’sComfortabilityand that external factors (e.g.,attitude towards robots or the robot’s perception) might affect it. On top of that, a list of 28 visual features associated with lowComfortabilitylevels is included, providing support to keep unravelingComfortabilityin further studies.

Country
Italy
Keywords

Affective computing, Comfortability, Human–robot interaction, Social errors

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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