论文信息 - Grasp recognition strategies from empirical models

Grasp recognition strategies from empirical models

A method for recognizing a part from a set of known parts using a parallel-jaw gripper and a simple sensor that measures the distance between the jaws is described. The authors consider how empirical measurements of part behavior can be used to generate efficient recognition strategies. These strategies are compared to random strategies using physical experiments. It is found that the former cut error rates and recognition times by approximately 50%.<<ETX>>

Kenneth Y. Goldberg | Dukhyun Kang

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