The chief tasks of robotic and prosthetic hands are grasping and manipulating objects, and size and weight constraints are very influential in their design. In this study, we use computational modeling to both predict and optimize the grasp quality of a reconfigurable, tendon-driven hand. Our computational results show that grasp quality, measured by the radius of the largest ball in wrench space, could be improved up to 259% by simply making some pulleys smaller and redistributing the maximal tensions of the tendons. We experimentally evaluated several optimized and unoptimized designs, which had either 4, 5, or 6 tendons and found that the theoretical calculations are effective at predicting grasp quality, with an average friction loss in this system of around 30%. We conclude that this optimization can be a very useful design tool and that using biologically inspired asymmetry and parameter adjustments can be used to maximize performance.

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