A taxonomy of everyday grasps in action
A taxonomy of everyday grasps in action
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日常行动的分类
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
N. Pollard
中科院分区：
文献类型：
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作者：
Jia Liu;Fangxiaoyu Feng;Yuzuko C. Nakamura;N. Pollard
抓取已经在机器人和人类学科文献中得到了很好的研究，并且已经开发了许多分类法来捕捉工作环境或日常生活中使用的抓取范围。但是这些分类法能完全捕捉到我们每天看到的抓取动作吗？我们要求两名受试者监测他们在一个典型的日子里用手做的每一个动作，以及对自我照顾、康复和各种职业重要的角色扮演动作，然后使用现有的分类法对所有抓取动作进行分类。虽然我们的受试者能够对许多抓取进行分类，但他们也发现了一组无法分类的抓取。此外，我们的研究对象观察到，分类法中的单个条目捕获的不是一次抓取，而是多次抓取。当我们进行调查时，我们发现这些抓取是通过与抓取动作相关的特征来区分的，比如预期的运动、力和刚度——这些特性也是机器人控制所需要的。我们建议一种增强抓取分类的格式，包括运动、力和刚度的特征，使用一种可以被轻度训练的主体理解和表达的语言，例如，用于注释示例或指导机器人。本文描述了我们的研究，结果，并记录了我们的注释数据库。
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.