DoughMaster: Demonstrate Once, Then Calibrate and Scale Dough Manipulation

Robot demonstrations are expensive, and simulation is a popular way to multiply them. This works well for rigid objects, but not for dough. Every press permanently changes the dough, so a simulator with the wrong material parameters drifts further from reality at every step of a long task. Calibrating the simulator usually requires extra data, such as dedicated probing motions or random exploration. We show that this extra data is unnecessary: the task demonstrations already contain everything needed to identify the material. Our key idea is to use the same demonstrations twice. We first replay them in a differentiable simulator to identify the physical parameters of the dough, and then generate many new demonstrations in the simulator they calibrated. To make identification work with real RGB-D data, we compare the simulation only with the surface each camera can see, and we supervise how the shape changes rather than its absolute position. The calibrated simulator then creates physically consistent variations in dough placement, camera pose, and appearance. On a real dual-arm robot shaping dough into letters, our method identifies material parameters that reproduce the observed deformations more accurately than a DPSI-style baseline using the same data (4.78 vs 5.10,mm Chamfer distance). With only two teleoperated demonstrations per letter, our augmented data trains a VLA policy that reaches a 54.4% success rate, compared with 24.4% when trained on five times as many real demonstrations.