论文/arXiv 2610.16667·16 天前

FEM验证的软触觉变形实时建模

针对软触觉传感器难以兼顾精度与实时性，结合少量实测验证、FEM与GNN，在平面软触觉结构上验证二维变形预测。

AI 整理

• 仿真与训练
• 数据与采集
• 端侧部署

原文链接：FEM-Validated Real-Time Modelling of Soft Tactile Deformation（arXiv 2610.16667 · 发布日期：2026-10-12）

FEM-Validated Real-Time Modelling of Soft Tactile Deformation

针对软触觉传感器难以兼顾精度与实时性，结合少量实测验证、FEM与GNN，在平面软触觉结构上验证二维变形预测。

核心要点

• 动机是FEM计算昂贵、实时仿真保真度不足，且脆弱传感器难以大量反复采集。
• 方法将验证与训练解耦，用82个相机跟踪接触配置调校验证ANSYS FEM，再生成768个合成场景训练GNN。
• 评估标记变形到接触定位的正向预测，以及接触配置到标记变形的反向推断。
• 经验证FEM在超90%验证案例中误差低于0.5 mm，GNN平均绝对误差0.32 mm且毫秒级推理。

验证范围与局限：验证范围限于平面软触觉结构的二维变形，包含82个实测接触配置和768个FEM合成接触场景。

英文摘要 (Abstract)

Accurate modelling of soft tactile sensors remains difficult because finite element methods (FEM) are computa- tionally expensive, while real-time simulators often lack fidelity for hyperelastic deformation. Large physical datasets are also impractical to collect because repeated testing can damage fragile sensors. We present a data-efficient framework that combines limited real-world validation, high-fidelity FEM, and graph neural networks (GNNs) for accurate real-time prediction of two-dimensional tactile deformation in a planar soft tactile structure. The key idea is to decouple validation from training: 82 camera-tracked contact configurations are used only to tune and validate an ANSYS FEM model, which then generates 768 synthetic contact scenarios for GNN training. We evaluate both forward prediction from marker deformation to contact localisation and inverse inference from contact configuration to marker deformation. The validated FEM achieves sub-0.5 mm error in more than 90% of validation cases, while the learned GNN surrogate reaches a mean absolute error of 0.32 mm with millisecond-scale inference, compared with minute-scale FEM simulation. Models trained entirely on synthetic FEM data retain sub-millimetre accuracy on real measurements from the 2D tactile structure, demonstrating strong sim-to-real transfer within the tested planar setting. We further integrate the learned model into a real-time visualisation pipeline, enabling rapid deformation prediction for virtual prototyping and simulator-based design exploration without extensive physical testing.

作者：Silvia Terrile, Kaichen Shi, Taejun Kwon, Jiseok Lee, Nathan F. Lepora, Yeejin Lee, Saekwang Nam, Efi Psomopoulou

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.16667：FEM-Validated Real-Time Modelling of Soft Tactile Deformation访问 2026-10-21