Data driven grasp synthesis using shape matching and task-based pruning

Ying Li, Jiaxin Fu, and Nancy Pollard
IEEE Transactions on Visualization and Computer Graphics, Vol. 13, 2007


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Abstract
Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments.

Notes
Sponsor: NSF
Grant ID: CCF-0343161, IIS-0326322,CNS-0423546, and ECS-0325383
Number of pages: 16

Text Reference
Ying Li, Jiaxin Fu, and Nancy Pollard, "Data driven grasp synthesis using shape matching and task-based pruning," IEEE Transactions on Visualization and Computer Graphics, Vol. 13, 2007

BibTeX Reference
@article{Li_2007_5686,
   author = "Ying Li and Jiaxin Fu and Nancy Pollard",
   title = "Data driven grasp synthesis using shape matching and task-based pruning",
   journal = "IEEE Transactions on Visualization and Computer Graphics",
   year = "2007",
   volume = "13",
}