Kai Furmans

MetaMVUC: Active Learning for Sample-Efficient Sim-to-Real Domain Adaptation in Robotic Grasping

Learning-based robotic grasping systems typically rely on large-scale datasets for training. However, collecting such datasets in the real-world is both costly and time-consuming. Synthetic data generation data is a cost-effective alternative, but models trained solely on synthetic data often strugg