AI RESEARCH
Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception
arXiv CS.CV
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ArXi:2605.05053v1 Announce Type: cross 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.