The body image of social robots
Bing Li, Oumayma Ajjaji, Robin Gigandet, Tatjana Nazir

TL;DR
This study analyzes human perceptions of social robots' appearance by examining spontaneous words associated with robots, revealing how attitudes, usage, and affect influence body image perceptions and emotional responses.
Contribution
It introduces a novel approach using word affect scales and embedding vectors to analyze human perceptions of robot body image over decades.
Findings
Valence and dominance reflect attitudes towards robots.
User base and usage influence perceptions.
Affects relate to semantic distances to 'person'.
Abstract
The rapid development of social robots has challenged robotics and cognitive sciences to understand humans' perception of the appearance of robots. In this study, robot-associated words spontaneously generated by humans were analyzed to semantically reveal the body image of 30 robots that have been developed over the past decades. The analyses took advantage of word affect scales and embedding vectors, and provided a series of evidence for links between human perception and body image. It was found that the valence and dominance of the body image reflected humans' attitude towards the general concept of robots; that the user bases and usages of the robots were among the primary factors influencing humans' impressions towards individual robots; and that there was a relationship between the robots' affects and semantic distances to the word ``person''. According to the results, building…
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Taxonomy
TopicsSocial Robot Interaction and HRI
