Grasp Performance

Robotic grasp performance research aims to enable robots to reliably and efficiently grasp a wide variety of objects in diverse environments. Current efforts focus on improving grasp detection and planning algorithms, often employing deep learning models like graph neural networks and capsule networks, as well as advanced control strategies incorporating visual and tactile feedback. These advancements are crucial for expanding robotic capabilities in areas such as manufacturing, agriculture, and assistive technologies, ultimately leading to more robust and versatile robotic systems.