PhysCoRe：用于材料感知可变形动力学的物理校正残差世界模型
PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
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中文总结 AI 辅助
针对可变形物体操作演变预测难题，提出PhysCoRe模型，结合可微MPM模拟器与两个前馈神经网络，通过MfM和RfD模块实现物理校正与残差学习，提升预测精度，其置信度分布为未来探索提供信号。
中文摘要 AI 辅助
预测可变形物体在机器人操作下的演变是一项长期挑战。现有方法通常依赖逐对象优化来拟合材料参数，速度慢且无法泛化，而端到端学习的方法外推性差且常违反基本物理结构。我们提出了PhysCoRe，一种物理校正残差世界模型，它将可微的物质点法（MPM）模拟器与两个前馈神经网络相结合。材料细化模块MfM从视觉观察中推断每个粒子的弹性，使模拟器基于特定对象的物理原理。残差校正模块RfD学习差异并预测对模拟器内部动力学的校正，吸收分析模型无法捕捉的系统偏差。实验表明PhysCoRe在预测精度上优于现有基线，其预测置信度在物体几何形状上形成可靠分布。
英文摘要
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.
发表机构
- Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。
