Social touch provides a rich non-verbal communication channel between humans and robots. Prior work has identified a set of touch gestures for human-robot interaction and described them with natural language labels (e.g., stroking, patting). Yet, no data exists on the semantic relationships between the touch gestures in users' minds. To endow robots with touch intelligence, we investigated how people perceive the similarities of social touch labels from the literature. In an online study, 45 participants grouped 36 social touch labels based on their perceived similarities and annotated their groupings with descriptive names. We derived quantitative similarities of the gestures from these groupings and analyzed the similarities using hierarchical clustering. The analysis resulted in 9 clusters of touch gestures formed around the social, emotional, and contact characteristics of the gestures. We discuss the implications of our results for designing and evaluating touch sensing and interactions with social robots.

翻译：社会性触摸为人机之间提供了一种丰富的非语言沟通渠道。已有研究识别出一组用于人机交互的触摸手势，并使用自然语言标签（如抚摸、轻拍）对其进行描述。然而，目前尚缺乏关于用户认知中触摸手势间语义关系的数据。为使机器人具备触摸智能，我们探究了人们如何感知文献中社会性触摸标签的相似性。在一项在线研究中，45名参与者根据感知相似性将36个社会性触摸标签进行分组，并为其分组标注描述性名称。我们从这些分组中推导出手势间的量化相似度，并采用层次聚类分析法对相似度进行分析。分析结果显示，触摸手势围绕其社会性、情感性及接触特性形成了9个聚类。我们讨论了这些结果对设计和评估社交机器人的触摸感知与交互的启示。