Clustering Social Touch Gestures for Human-Robot Interaction
Pages 53 - 67
Abstract
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.
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Index Terms
- Clustering Social Touch Gestures for Human-Robot Interaction
Index terms have been assigned to the content through auto-classification.
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Published In
Dec 2023
444 pages
ISBN:978-981-99-8714-6
DOI:10.1007/978-981-99-8715-3
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 03 December 2023
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