Abstract
Visuotactile sensors typically employ sparse marker arrays that limit spatial resolution and lack clear analytical force-to-image relationships. To solve this problem, we present \textbf{MoiréTac}, a dual-mode sensor that generates dense interference patterns via overlapping micro-gratings within a transparent architecture. When two gratings overlap with misalignment, they create moiré patterns that amplify microscopic deformations. The design preserves optical clarity for vision tasks while producing continuous moiré fields for tactile sensing, enabling simultaneous 6-axis force/torque measurement, contact localization, and visual perception. We combine physics-based features (brightness, phase gradient, orientation, and period) from moiré patterns with deep spatial features. These are mapped to 6-axis force/torque measurements, enabling interpretable regression through end-to-end learning. Experimental results demonstrate three capabilities: force/torque measurement with R^2 > 0.98 across tested axes; sensitivity tuning through geometric parameters (threefold gain adjustment); and vision functionality for object classification despite moiré overlay. Finally, we integrate the sensor into a robotic arm for cap removal with coordinated force and torque control, validating its potential for dexterous manipulation.
Abstract (translated)
视觉触觉传感器通常使用稀疏的标记阵列，这限制了空间分辨率，并且缺乏明确的力到图像的关系。为了解决这个问题，我们提出了**MoiréTac**，这是一种双模式传感器，它通过透明架构内的微栅格重叠生成密集的干涉图案。当两个栅格以不对齐的方式重叠时，它们会产生莫尔条纹，放大微观变形。这种设计在保持光学清晰度的同时用于视觉任务，并产生连续的莫尔场用于触觉感应，从而能够同时进行六轴力/扭矩测量、接触定位和视觉感知。 我们结合了来自莫尔图案的基于物理特性的特征（亮度、相位梯度、方向和周期）与深度空间特性。这些特性被映射到六轴力/扭矩测量上，通过端到端学习实现了可解释的回归分析。实验结果展示了三个能力：在所有测试轴上的力/扭矩测量 R^2 > 0.98；通过几何参数（三倍增益调整）进行灵敏度调节；即使存在莫尔图案覆盖的情况下也能实现物体分类的视觉功能。 最后，我们将传感器集成到机器人手臂上用于瓶盖去除操作，并实现了协调的力和扭矩控制，验证了其在灵巧操作中的潜力。
URL
https://arxiv.org/abs/2509.12714
