Grasp Identification and Synthesis Using Static Grasp Pose

Abstract In this paper, we propose a method for analyzing and synthesizing human's grasping motion. For given joint data sets, we apply the PCA technique for dimension reduction and the Gaussian mixture model(GMM) for identifying grasp types and the associated object parameters. And we compare the proposed method with other grasp identification methods. The validity of the identification and synthesis method are verified through simulations with human grasp data captured by a data glove.

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