Augmented Dexterity

Augmented dexterity research aims to enhance human capabilities in complex manipulation tasks, particularly in surgery, by integrating robotic systems and advanced computational methods. Current efforts focus on developing robust simulation environments for training and testing algorithms, incorporating AI models like large language models for high-level planning and control of surgical robots, and utilizing computer vision techniques such as hand pose estimation for automated feedback in surgical training. This work holds significant promise for improving surgical precision, efficiency, and training, ultimately leading to better patient outcomes and advancing the field of robotic surgery.