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  1. 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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  2. Designing robotic hands has been an active area of research and innovation in the last decade. However, little is known about how people perceive robot hands and react to being touched by them. To inform hand design for social robots, we created a database of 73 robot hands and ran two user studies. In the first study, 160 online users rated the hands in our database. Variations in user ratings mostly centered on the perceived Comfortableness, Interestingness, and Industrialness of the hands. In a second lab-based study, users evaluated seven physical hands and had similar ratings to results from the online study. Furthermore, we did not find a significant difference in user ratings before and after the users were touched by the hands. We provide regression models that can predict user ratings from the hand features (e.g., number of fingers) and an online interface for using our robot hand database and predictive models. 
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  3. Recent robot collections provide various interactive tools for users to explore and analyze their datasets. Yet, the literature lacks data on how users interact with these collections and which tools can best support their goals. This late-breaking report presents preliminary data on the utility of four interactive tools for accessing a collection of robot hands. The tools include a gallery and similarity comparison for browsing and filtering existing hands, a prediction tool for estimating user impression of hands (e.g., humanlikeness), and a recommendation tool suggesting design features (e.g., number of fingers) for achieving a target user impression rating. Data from a user study with 9 novice robotics researchers suggest the users found the tools useful for various tasks and especially appreciated the gallery and recommendation functionalities for understanding the complex relationships of the data. We discuss the results and outline future steps for developing interface design guidelines for robot collections. 
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