AutoRSR: Automated Real-to-Sim-to-Real Data Generation for Scalable Robot Learning

Scaling embodied robot learning requires large and diverse manipulation datasets, but collecting real-world demonstrations remains expensive in human effort and robot time. Simulation provides a scalable alternative, yet converting a real workspace into a robot-learning-ready environment still requires substantial manual effort for object extraction, asset processing, coordinate calibration, and trajectory specification.

From the abstract. Our summary is in progress.