用物理模型指导深度学习，提升触觉成像精度与可靠性
Physics-Driven Learning Framework for Tomographic Tactile Sensing
- 将EIT正向物理模型嵌入损失函数，约束网络输出
- 16电极实验显示重建形状更清晰、误差更低
- 适合需要高精度触觉感知的机器人应用
电气阻抗断层扫描（EIT）因其布线少、柔性好，是大面积极触觉传感的有力方案，但其非线性逆问题常导致严重伪影和接触重建不准确。本文提出PhyDNN，一种物理驱动的深度重建框架，将EIT正向模型直接嵌入学习目标。通过联合最小化预测与真实电导率图之间的差异，并强制满足正向偏微分方程（PDE）的一致性，PhyDNN降低了深度网络的黑箱特性，提升了物理合理性与泛化能力。为实现高效反向传播，设计了一个可微分的前向算子网络，精确逼近非线性EIT响应，支持快速物理引导训练。在16电极柔性传感器上的大量仿真与真实触觉实验表明，PhyDNN在接触形状、位置和压力分布重建上持续优于NOSER、TV和标准DNN，具有更少伪影、更锐利边界和更高指标得分，验证了其在高质量断层触觉传感中的有效性。
原文摘要 · Abstract (English)
Electrical impedance tomography (EIT) provides an attractive solution for large-area tactile sensing due to its minimal wiring and shape flexibility, but its nonlinear inverse problem often leads to severe artifacts and inaccurate contact reconstruction. This work presents PhyDNN, a physics-driven deep reconstruction framework that embeds the EIT forward model directly into the learning objective. By jointly minimizing the discrepancy between predicted and ground-truth conductivity maps and enforcing consistency with the forward PDE, PhyDNN reduces the black-box nature of deep networks and improves both physical plausibility and generalization. To enable efficient backpropagation, we design a differentiable forward-operator network that accurately approximates the nonlinear EIT response, allowing fast physics-guided training. Extensive simulations and real tactile experiments on a 16-electrode soft sensor show that PhyDNN consistently outperforms NOSER, TV, and standard DNNs in reconstructing contact shape, location, and pressure distribution. PhyDNN yields fewer artifacts, sharper boundaries, and higher metric scores, demonstrating its effectiveness for high-quality tomographic tactile sensing.
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