Physics-grounded simulation can replace expensive real-world data collection for deformable object manipulation—synthetic data from calibrated digital twins trains policies that work in the real world without additional real-world training.
SIM1 creates physics-accurate digital twins of deformable objects from real demonstrations, then generates synthetic training data through simulation to train robotic manipulation policies. The system achieves real-world performance comparable to policies trained on 15x more real data, solving the data scarcity problem for cloth and soft object manipulation.