Gripper Object

Gripper object research focuses on enabling robots to robustly grasp and manipulate a wide variety of objects. Current efforts concentrate on developing efficient algorithms for grasp planning and synthesis, often employing techniques like variational autoencoders, augmented Lagrangian formulations, and Koopman operator theory to model complex gripper-object interactions, including those involving external contacts. These advancements aim to improve the success rate and dexterity of robotic grasping, impacting fields such as robotics, automation, and manufacturing through more versatile and reliable robotic manipulation.