Functional Grasp

Functional grasping research aims to enable robots to grasp objects in ways that are suitable for subsequent manipulation tasks, mirroring human dexterity and adaptability. Current efforts focus on developing robust and generalizable models, often employing deep reinforcement learning, diffusion models, and graph neural networks, to synthesize grasps for diverse objects and robotic hands, often incorporating force feedback and multi-objective optimization. This research is crucial for advancing robotic manipulation capabilities, impacting fields like manufacturing, assistive robotics, and human-robot interaction by enabling robots to perform complex tasks requiring precise and functional grasps. The development of large-scale datasets and shared control frameworks further enhances the field's progress.