PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics

2026-07-22

AI Summary

This work addresses the challenges of physical inconsistency and poor generalization in dynamic prediction for deformable objects by proposing a material-aware, physics-corrected residual world model. The approach integrates a differentiable Material Point Method (MPM) physics simulator with two lightweight feedforward networks: Material from Motion (MfM) infers particle-level elastic parameters from visual inputs to enable online material identification for novel objects, while Residual from Dynamics (RfD) learns and corrects systematic simulation biases. Coupled with an uncertainty-guided active exploration mechanism, the model maintains physical consistency while achieving strong generalization. Experiments demonstrate that the method outperforms state-of-the-art approaches in prediction accuracy on real-world deformable object manipulation sequences, with well-calibrated confidence estimates that effectively support downstream decision-making.

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.

Problem

• deformable dynamics
• material-aware prediction
• physics-based simulation
• robotic manipulation
• world models

Innovation

• Physics-Corrected Residual
• Material Point Method
• Material from Motion
• Residual from Dynamics
• Deformable Object Dynamics