Category Level Manipulation

Category-level manipulation focuses on teaching robots to perform tasks on objects of a certain type, generalizing learned skills to new, unseen instances within that category. Current research emphasizes learning these skills from limited data, such as single demonstrations or unlabeled videos, often employing dynamic graph convolutional networks or object-centric representations to achieve this generalization. This research is significant because it reduces the need for extensive manual programming and data annotation, paving the way for more efficient and adaptable robotic systems in various applications, including industrial assembly and other complex manipulation tasks.