发表机构
UIUC; Columbia University（伊利诺伊大学厄巴纳 - 香槟分校; 哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对可变形物体模拟中动力学预测难的问题，提出物理引导残差动力学（PGRD）框架，结合物理与学习方法优势，采用特定架构和公式，在多物体模拟上结果更准，还展示了其在操作规划和交互式模拟中的应用效用。
AI 中文摘要
模拟可变形物体对众多机器人操作应用至关重要，但准确预测其动力学仍具挑战性。我们提出物理引导残差动力学（PGRD），这是一个结合基于物理和基于学习方法优势的混合模拟框架。具体而言，PGRD将可优化的弹簧质量模拟器作为主干，与预测基于物理预测的残差校正的神经网络相结合。我们采用基于速度的公式确保稳定模拟，并使用滑动窗口变压器架构捕捉时间依赖性。我们表明，在一组不同的真实世界可变形物体上，PGRD比纯基于物理和基于学习的方法产生更准确的结果。我们还在两个应用中展示了PGRD的效用：通过模型预测控制进行操作规划，包括具有生成目标图像的语言条件设置；以及通过3D高斯点渲染进行基于动作条件的视频预测的交互式模拟。
英文摘要
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.
CommentsWebsite: https://pgrd-robot.github.io/
