Grasp Quality

Grasp quality assessment focuses on determining how effectively a robotic gripper can secure an object, considering factors like contact stability and force closure. Current research emphasizes developing efficient and accurate grasp planning algorithms, often employing neural networks (like UNets and EquiFormers) and differentiable optimization techniques to rapidly identify high-quality grasps from various viewpoints and for diverse gripper types. This work is crucial for advancing robotic manipulation in unstructured environments, improving the reliability and speed of tasks like bin picking and human-robot handover, and enabling more versatile robotic systems.