Learning Physics-Guided Residual Dynamics for Deformable Object Simulation

• URL: http://arxiv.org/abs/2607.13451v1
• Date: Wed, 15 Jul 2026 05:15:43 GMT
• Title: Learning Physics-Guided Residual Dynamics for Deformable Object Simulation
• Abstract summary: Physics-Guided Residual Dynamics (PGRD) is a hybrid simulation framework that combines the advantages of physics-based and learning-based approaches.<n>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.
• Score: 19.104444935269616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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