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Haptic Object Recognition Using A Multi-Fingered Dextrous Hand

Allen, Peter K.; Roberts, Kenneth S.

It is becoming increasingly clear that robotic systems need to have capabilities similar to the human haptic system in order to perform complex grasping, manipulation and object recognition tasks using dextrous hands. This paper is an exploration of using a dextrous, multi-fingered hand for high-level object recognition tasks. The paradigm is model-base recognition in which the objects are modeled and recovered as superquadrics, which are shown to have a number of important attributes that make them well suited for such a task. Experiments have been performed to recover the shape of objects using sparse contact point data from the hand with promising results. We also present our approach to using tactile data in conjunction with the dextrous hand to build a library of grasping and exploration primitives that can be used in recognizing and grasping more complex multi-part object.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-363-88
Published Here
December 19, 2011
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