RGB sim-to-real for deformable manipulation has remained largely unsolved without real-world fine-tuning. We present SimWeaver, which trains zero-shot RGB VLA policies on 200 simulated demonstrations per task, reaching above 80% per-task and 91% average real-world success across 5 diverse deformable tasks including plastic-bag manipulation, without teleoperation or per-task calibration. SimWeaver combines a reliable measurement-backed simulator (SimWeaver-Sim) with an extensible asset framework supporting single-image generation(SimWeaver-Asset), a deterministic topology-aware trajectory synthesizer (SimWeaver-Syn), and a sim-to-real protocol with ISP-aware photometric augmentation (SimWeaver-Real). On silk grasping, the sim-trained policy reaches 100% under visual distribution shifts where real-data baselines drop to 9-70%, at two orders of magnitude lower per-trajectory cost. We will release SimWeaver and a representative asset subset. Project page: https://simweaver.github.io/

翻译：RGB仿真到现实迁移在柔性物体操控领域至今仍基本未解决，且需依赖真实世界微调。我们提出SimWeaver，该方法在每个任务仅使用200个仿真演示即可训练零样本RGB VLA策略，在塑料袋操控等5个不同柔性物体任务中，分别达到超过80%的单任务成功率与91%的平均真实世界成功率，无需遥操作或逐任务校准。SimWeaver整合了具备可靠测量支持的仿真器（SimWeaver-Sim）、支持单图生成的可扩展资产框架（SimWeaver-Asset）、确定性拓扑感知轨迹合成器（SimWeaver-Syn），以及包含ISP感知光度增强的仿真到现实迁移协议（SimWeaver-Real）。在丝绸抓取任务中，经仿真训练的策略在视觉分布偏移下实现100%成功率，而基于真实数据训练的基线方法成功率降至9-70%，且每条轨迹成本降低两个数量级。我们将开源SimWeaver及其代表性资产子集。项目页面：https://simweaver.github.io/