Function-Preserving Data Generation for Zero-Shot Real-to-Sim-to-Real Manipulation

Robotic data generation is a promising paradigm for scaling robot learning without collecting large-scale real-world data. However, generating geometrically diverse yet physically valid data for contact-rich tasks remains challenging, especially when success depends on precise geometric interfaces.