Dexterous Grasping

Dexterous grasping research aims to enable robots to manipulate objects with the dexterity and adaptability of human hands, focusing on robust and versatile grasping across diverse objects and environments. Current research heavily utilizes deep learning models, including generative adversarial networks (GANs), transformers, and diffusion models, often coupled with reinforcement learning to synthesize and refine grasps, and incorporating tactile feedback for improved control and adaptability. This field is crucial for advancing robotics in various sectors, from industrial automation and assistive technologies to exploration and manipulation in unstructured environments.