用神经网络加速高精度触觉模拟，又快又省内存。
Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception
- 用粗粒度MPM加神经解码器，从低维状态还原精细触觉细节。
- 比TacIPC快65%以上，内存降低40%，几何保真度更高。
- 适合需要快速真实触觉反馈的机器人操作与优化任务。
触觉感知对灵巧操作至关重要，但高分辨率弹性体变形模拟仍计算成本高昂。有限元方法（FEM）虽精度高却需频繁重网格，材料点法（MPM）则面临粒子-内存的权衡。本文提出一种降阶神经模拟框架，将粗粒度MPM动力学与隐式神经解码器结合，从紧凑潜空间重建亚粒子级触觉细节。该框架从高低分辨率模拟配对数据中学习连续形变流形，实现物理一致且可微推断。相比TacIPC，本方法模拟速度提升超65%，内存使用降低40%，同时保持更优几何保真度。在触觉渲染与三维表面重建中，精度提升25%，可在更快推理速度下生成逼真的深度图与表面网格。结果表明，该降阶神经模型为机器人交互与优化提供了高效、物理可信的高细节触觉模拟方案。
原文摘要 · Abstract (English)
Tactile perception is key to dexterous manipulation, yet simulating high-resolution elastomer deformation remains computationally prohibitive. Finite element methods (FEM) deliver high fidelity but demand costly remeshing, while Material Point Methods (MPM) suffer from heavy particle-memory tradeoffs. We propose a {reduced-order neural simulation framework} that couples coarse-grained MPM dynamics with an implicit neural decoder to reconstruct sub-particle tactile details from compact latent states. The framework learns a continuous deformation manifold from paired high- and low-resolution simulations, enabling physically consistent, differentiable inference. Compared to the TacIPC, our method achieves over 65\% faster simulation and {40\% lower memory usage}, while maintaining better geometric fidelity. In tactile rendering and 3D surface reconstruction, our methods further improve accuracy by 25\% and produce realistic depth images and surface mesh within a faster inference speed. These results demonstrate that the proposed reduced-order neural model enables high-detail, physically grounded tactile simulation with substantial efficiency gains for robotic interaction and optimization.
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