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arXiv 2302.03219cs.ROq-bio.NC
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社交机器人的身体意象

The body image of social robots

SCALAB, UMR CNRS 9193(法国国家科学研究中心SCALAB联合研究单位(UMR 9193));Université de Lille(里尔大学)

Bing Li, Oumayma Ajjaji, Robin Gigandet, Tatjana Nazir

AI总结:

本文通过分析人类自发产生的词语,从语义层面揭示30个社交机器人的身体意象,发现其情感维度、用户基础及与“人”的语义距离共同影响人类对机器人的感知和态度。

摘要:

社交机器人的快速发展对机器人学和认知科学提出了挑战,要求理解人类对机器人外观的感知。本研究分析了人类自发产生的与机器人相关的词语,以语义方式揭示过去几十年间开发的30个机器人的身体意象。分析利用了词语情感量表和嵌入向量,并为人类感知与身体意象之间的联系提供了一系列证据。研究发现,身体意象的效价和支配度反映了人类对机器人总体概念的态度;机器人的用户基础和使用场景是影响人类对单个机器人印象的主要因素之一;并且机器人的情感与到“人”一词的语义距离之间存在关系。根据这些结果,为机器人构建身体意象是研究哪些特征受到人们欣赏以及什么因素影响人们对机器人的感受的有效范式。

英文摘要:

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 body image for robots was an effective paradigm to investigate which features were appreciated by people and what influenced people's feelings towards robots.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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