Functional Grasping

Functional grasping research aims to enable robots to grasp and manipulate objects effectively, mirroring human dexterity and adaptability. Current efforts concentrate on developing robust and generalizable grasping policies using reinforcement learning, novel geometric representations (e.g., point clouds, graph neural networks), and large language models to incorporate semantic understanding of tasks and objects. This field is crucial for advancing robotics in diverse applications, from assistive devices and surgery to warehouse automation and household robotics, by improving the reliability and efficiency of robotic manipulation.