Robotic Gripper

Robotic grippers are end-effectors designed to enable robots to grasp and manipulate objects, a crucial aspect of achieving autonomous manipulation. Current research emphasizes improving gripper dexterity and adaptability through innovative designs incorporating soft materials, multiple degrees of freedom, and integrated sensing (e.g., vision-based tactile sensors). This involves developing advanced control algorithms, such as those based on deep reinforcement learning and Bayesian inference, to optimize grasping strategies and handle diverse object shapes and properties. These advancements are significant for expanding robotic capabilities in various fields, including manufacturing, agriculture, and assistive robotics.