Robot Gripper

Robot grippers are robotic end-effectors designed to grasp and manipulate objects, with research focusing on improving their dexterity, adaptability, and control. Current efforts involve developing advanced mathematical models for precise force and motion control, creating customizable gripper designs for diverse object geometries using techniques like Boolean operations, and employing machine learning algorithms, particularly Quality-Diversity methods, to generate diverse and effective grasping strategies. These advancements are crucial for enhancing automation in manufacturing, logistics, and other fields where reliable object manipulation is essential, particularly for complex tasks like assembly and material sorting.