Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception
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 m...
| Authors | Yuhu Guo, Zhikai Shen, Jiasheng Qu et al. |
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| Primary category | cs.RO |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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