A taxonomy of everyday grasps in action

Grasping has been well studied in the robotics and human subjects literature, and numerous taxonomies have been developed to capture the range of grasps employed in work settings or everyday life. But how completely do these taxonomies capture grasping actions that we see every day? We asked two subjects to monitor every action that they performed with their hands during a typical day, as well as to role-play actions important for self-care, rehabilitation, and various careers and then to classify all grasping actions using existing taxonomies. While our subjects were able to classify many grasps, they also found a collection of grasps that could not be classified. In addition, our subjects observed that single entries in the taxonomy captured not one grasp, but many. When we investigated, we found that these grasps were distinguished by features related to the grasping action, such as intended motion, force, and stiffness - properties also needed for robot control. We suggest a format for augmenting grasp taxonomies that includes features of motion, force, and stiffness using a language that can be understood and expressed by subjects with light training, as would be needed, for example, for annotating examples or coaching a robot. This paper describes our study, the results, and documents our annotated database.