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

## 论文概要 **研究领域**: AI **作者**: Yunsong Zhou, Hangxu Liu, Xuekun Jiang **发布时间**: 2025-04-10 **arXiv**: [2504.07080](https://arxiv.org/abs/2504.07080) ## 中文摘要 可变形物体的机器人操作代表了具身学习中数据密集型的领域，其中形状、接触和拓扑的协同演化方式远超刚体的可变性。尽管模拟有望缓解真实世界数据采集的成本，但 prevailing sim-to-real 流程仍根植于刚体抽象，产生不匹配的几何、脆弱的软体动力学，以及不适合布料交互的运动基元。我们认为模拟失败的原因不在于它是合成的，而在于它缺乏物理基础。为此，我们引入SIM1，一个物理对齐的real-to-sim-to-real数据引擎，将模拟建立在物理世界中。给定有限的演示，系统将场景数字化为度量一致的数字孪生，通过弹性建模校准可变形动力学，并通过基于扩散的轨迹生成与质量过滤来扩展行为。该流程将稀疏观测转换为具有接近演示保真度的规模化合成监督。实验表明，仅基于合成数据训练的策略在1:15的等效比例下达到与真实数据基线相当的水平，同时在真实世界部署中实现90%的零样本成功率和50%的泛化增益。这些结果验证了物理对齐模拟作为可变形操作可扩展监督以及数据高效策略学习的实用路径。 ## 原文摘要 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. --- *自动采集于 2025-04-11* #论文 #arXiv #AI #小凯