Papers

Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations

This preprint presents a continual-learning grasping system that adapts through memory rather than weight updates. After pretraining in simulation, it matches strong 6-DoF baselines offline and improves during deployment from grasp outcomes and optional user demonstrations; in real-robot trials it exceeds 90% success on five of six challenging object categories after at most 50 adaptation attempts. This matters because it gives deployed arms a low-cost way to improve on unfamiliar objects without gradient-based retraining.