SIM1：物理对齐模拟器在可变形世界中的零样本数据扩展应用

摘要

可变形物体的机器人操控在具身学习中属于数据密集型领域，其形状、接触状态和拓扑结构的协同演化远超刚体对象的可变性。尽管仿真技术有望缓解真实世界数据采集的成本问题，但主流仿真到现实流水线仍基于刚体抽象模型，导致几何失配、软体动力学脆弱以及难以适应布料交互的运动基元。我们认为仿真技术的缺陷并非源于其合成属性，而是因其缺乏物理根基。为此我们提出SIM1：一种基于物理对齐的真实-仿真-真实数据引擎，将仿真系统锚定在物理世界中。该系统通过有限演示样本将场景数字化为度量一致的双生模型，通过弹性建模校准可变形物体动力学，并利用基于扩散模型的轨迹生成与质量过滤机制扩展行为模式。该流水线能将稀疏观察转化为具有近演示保真度的规模化合成监督信号。实验表明，基于纯合成数据训练的策略在1:15的等效比下达到真实数据基线的同等性能，在现实部署中实现90%的零样本成功率及50%的泛化提升。这些结果验证了物理对齐仿真可作为可变形物体操控的可扩展监督方案，为数据高效策略学习提供了可行路径。

English

Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the variability of rigids. Although simulation promises relief from the cost of real-world data acquisition, prevailing sim-to-real pipelines remain rooted in rigid-body abstractions, producing mismatched geometry, fragile soft dynamics, and motion primitives poorly suited for cloth interaction. We posit that simulation fails not for being synthetic, but for being ungrounded. To address this, we introduce SIM1, a physics-aligned real-to-sim-to-real data engine that grounds simulation in the physical world. Given limited demonstrations, the system digitizes scenes into metric-consistent twins, calibrates deformable dynamics through elastic modeling, and expands behaviors via diffusion-based trajectory generation with quality filtering. This pipeline transforms sparse observations into scaled synthetic supervision with near-demonstration fidelity. Experiments show that policies trained on purely synthetic data achieve parity with real-data baselines at a 1:15 equivalence ratio, while delivering 90% zero-shot success and 50% generalization gains in real-world deployment. These results validate physics-aligned simulation as scalable supervision for deformable manipulation and a practical pathway for data-efficient policy learning.

SIM1：物理对齐模拟器在可变形世界中的零样本数据扩展应用

SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

摘要

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