PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
PhysCoRe is a physics-corrected residual world model that couples a differentiable Material Point Method (MPM) simulator with two feed-forward neural networks to infer per-particle elasticity from visual observations and learn corrections for simulator biases, significantly improving prediction accuracy for deformable objects under robotic manipulation and enabling online material identification with confidence-guided exploration.
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[Submitted on 22 Jul 2026]
Title:PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
View a PDF of the paper titled PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics, by Haocheng Yin and 3 other authors
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Abstract:Predicting how deformable objects evolve under robotic manipulation is a longstanding challenge. Existing approaches typically rely on per-object optimization to fit material parameters, which can be slow and cannot generalize, while end-to-end learned alternatives extrapolate poorly and often violate basic physical structure. We present PhysCoRe, a physics-corrected residual world model that couples a differentiable Material Point Method (MPM) simulator with two feed-forward neural networks. A material refinement module, Material from Motion (MfM), infers per-particle elasticity from visual observations, grounding the simulator in object-specific physics. A residual correction module, Residual from Dynamics (RfD), learns the discrepancy and predicts corrections to the simulator's internal dynamics, absorbing systematic biases that the analytical model cannot capture. This design also supports online material identification on novel objects. MfM adapts from limited interactions, and its predictive uncertainty steers further exploration toward the regions where its estimate is least confident. Experiments on real deformable-object manipulation sequences show that PhysCoRe outperforms state-of-the-art baselines in prediction accuracy, and that its predicted confidence forms a reliable distribution across the object's geometry, providing a natural signal for future confidence-guided exploration.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.20653 [cs.RO]
(or arXiv:2607.20653v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20653
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haocheng Yin [view email] [v1] Wed, 22 Jul 2026 18:25:57 UTC (2,768 KB)
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