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Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation
2017
Journal of Intelligent and Robotic Systems
In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both selected gripper mechanism parameters, and parameters of the finger geometry. We suggest six gripper quality indices that indicate different aspects of the performance of a gripper given a CAD model of an object and a task description. These quality indices are then used to learn task-specific finger designs based on dynamic simulation. We demonstrate our gripper optimization on a
doi:10.1007/s10846-017-0492-y
fatcat:hxbiq4uxgfhvbh3rum3tmw3upe