Large-scale robot datasets have facilitated the learning of a wide range of robot manipulation skills, but these datasets remain difficult to collect and scale further, owing to the intractable amount of human time, effort, and cost required. Simulation and synthetic data generation have proved to be an effective alternative to fuel this need for data, especially with the advent of recent work that shows that such synthetic datasets can dramatically reduce real-world data requirements and facilitate generalization to novel scenarios unseen in the real-world demonstrations. However, this paradigm has been limited to rigid-body tasks, which are easy to simulate. Deformable object manipulation encompasses a large portion of real-world manipulation and remains a crucial gap to address towards increasing adoption of the synthetic simulation data paradigm. In this paper, we introduce SoftMimicGen, an automated data generation pipeline for deformable object manipulation tasks. We introduce a suite of high-fidelity simulation environments that encompasses a wide range of deformable objects (stuffed animal, rope, tissue, towel) and manipulation behaviors (high-precision threading, dynamic whipping, folding, pick-and-place), across three different robot embodiments (robot arm, humanoid, surgical robot). We apply SoftMimicGen to generate datasets across the task suite and train high-performing agents from the data, and systematically analyze the data generation system.
