对比多种仿真参数估计方法，评估其在机器人布料操作中的表现。
Can Real-to-Sim Approaches Capture Dynamic Fabric Behavior for Robotic Fabric Manipulation?
- 采用物理信息神经网络（PINN）等三种先进方法进行参数估计。
- 不同仿真引擎和方法对动态布料操作性能影响显著，静态任务中PINN更优。
- 适用于需要高精度布料模拟的机器人抓取与操控研究者。
本文系统评估了机器人布料操作中真实到仿真（Real-to-Sim）参数估计方法的有效性。研究对比了三种前沿方法：两种微分管道与一种数据驱动方法，并提出一种新型物理信息神经网络（PINN）用于物理参数估计。这些方法在多种真实到仿真场景（提起、风吹、拉伸）下，针对五种不同布料类型进行了测试，并在三个未见场景（折叠、甩动、摇晃）中评估性能。结果表明，仿真引擎和所选方法对布料操作效果有显著影响；其中，PINN在准静态任务中表现优异，但在动态场景中存在局限性。
原文摘要 · Abstract (English)
This paper presents a rigorous evaluation of Real-to-Sim parameter estimation approaches for fabric manipulation in robotics. The study systematically assesses three state-of-the-art approaches, namely two differential pipelines and a data-driven approach. We also devise a novel physics-informed neural network approach for physics parameter estimation. These approaches are interfaced with two simulations across multiple Real-to-Sim scenarios (lifting, wind blowing, and stretching) for five different fabric types and evaluated on three unseen scenarios (folding, fling, and shaking). We found that the simulation engines and the choice of Real-to-Sim approaches significantly impact fabric manipulation performance in our evaluation scenarios. Moreover, PINN observes superior performance in quasi-static tasks but shows limitations in dynamic scenarios.
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