Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning
IEEE Transactions on Visualization and Computer Graphics
Biologically-inspired 3D grasp synthesis based on visual exploration
Autonomous Robots
Enabling grasping of unknown objects through a synergistic use of edge and surface information
International Journal of Robotics Research
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This paper presents and analyzes 12 quality measures that characterize robotic grips according to their stability and reliability. The measures are designed to assess three-finger grips of two-dimensional parts performed in a real environment, taking into account both theoretical aspects and unavoidable uncertainties of a grasping action. They build on the existing literature and on physical and mechanical considerations. The measures constitute a feature space that pattern recognition methods can use in order to classify robotic grips according to their quality. Six of the measures depend on the actual finger configuration of the gripper, and they have shown to be critical for better characterization. The kinematics of the Barrett Hand have been used. As a validation step, the measures are merged in two global quality values (with different practical applicability) that can be used to rank feasible candidate grips.