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.

翻译：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汇，以语义层面揭示过去几十年中开发的30种机器人的身体意象。分析利用了词汇情感量表和嵌入向量，并提供了一系列证据表明人类感知与身体意象之间的联系。研究发现，身体意象的效价和优势度反映了人类对机器人总体概念的态度；机器人的用户群体和用途是影响人类对个体机器人印象的主要因素之一；机器人的情感与“人”这一词汇的语义距离之间存在关联。根据研究结果，为机器人构建身体意象是一种有效的范式，可用于探究哪些特征受到人们青睐，以及哪些因素影响人们对机器人的感受。