Proceedings Article10.1109/HUMANOIDS.2014.7041420
A taxonomy of everyday grasps in action
Jia Liu,Fangxiaoyu Feng,Yuzuko C. Nakamura,Nancy S. Pollard +3 more
- 01 Nov 2014
- pp 573-580
TL;DR: 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.
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Abstract: 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.
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Citations
The GRASP Taxonomy of Human Grasp Types
TL;DR: The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition and is shown that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more generalgrasps if only the hand configuration is considered without the object shape/size.
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GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes
Enric Corona,Albert Pumarola,Guillem Alenyà,Francesc Moreno-Noguer,Grégory Rogez +4 more
- 14 Jun 2020
TL;DR: A generative model is introduced that jointly reasons in all levels and refines the 51-DoF of a 3D hand model that minimize a graspability loss, and can robustly predict realistic grasps, even in cluttered scenes with multiple objects in close contact.
Understanding Everyday Hands in Action from RGB-D Images
Grégory Rogez,James Steven Supancic,Deva Ramanan +2 more
- 07 Dec 2015
TL;DR: A large dataset of 12,000 RGB-D images covering 71 everyday grasps in natural interactions allowing for exploration of contact and force prediction from perceptual cues and illustrating the role of segmentation, object context, and 3D-understanding in functional grasp analysis.
Grasp and dexterous manipulation of multi-fingered robotic hands: a review from a control view point
Ryuta Ozawa,Kenji Tahara +1 more
TL;DR: This paper investigates past research studies on control systems of multi-fingered robotic hands for grasping and manipulation in robotics.
119
•Posted Content
CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction.
TL;DR: An explicit contact representation namely Contact Potential Field (CPF), and a learning-fitting hybrid framework namely MIHO to Modeling the Interaction of Hand and Object are presented.
83
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