用物理引导的残差网络，让软体物体模拟更准更快。
Learning Physics-Guided Residual Dynamics for Deformable Object Simulation
- 结合物理模型与神经网络，修正物理预测偏差
- 在多种真实软体物体上精度优于纯物理或纯学习方法
- 适合机器人操控规划和交互式模拟场景
软体物体模拟对机器人操作应用至关重要，但准确预测其动力学仍具挑战。本文提出物理引导残差动力学（PGRD），一种融合物理驱动与学习驱动优势的混合仿真框架。PGRD以可优化的弹簧-质量模型为骨干，搭配一个神经网络预测物理预测的残差修正。采用速度形式保证仿真稳定，并使用滑动窗口变换器捕捉时间依赖性。实验表明，PGRD在多种真实软体物体上的表现优于纯物理或纯学习方法。进一步验证了其在两种应用场景中的有效性：基于模型预测控制的操纵规划（包括语言条件下的目标图像生成）；以及通过3D高斯溅射实现的动作条件视频预测的交互式模拟。
原文摘要 · Abstract (English)
Simulating deformable objects is essential for a wide range of robotic manipulation applications, yet accurately predicting their dynamics remains challenging. We propose Physics-Guided Residual Dynamics (PGRD), a hybrid simulation framework that combines the advantages of physics-based and learning-based approaches. Specifically, PGRD combines an optimizable spring-mass simulator as a backbone with a learned neural network that predicts residual corrections to the physics-based predictions. We adopt a velocity-based formulation to ensure stable simulation and a sliding-window transformer architecture to capture temporal dependencies. We show that PGRD produces more accurate results than both purely physics-based and learning-based methods on a set of diverse real-world deformable objects. We further demonstrate the utility of PGRD in two applications: manipulation planning via Model Predictive Control, including a language-conditioned setting with a generated goal image; and interactive simulation via action-conditioned video prediction by 3D Gaussian Splatting.
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