Vision-Based Tactile Sensors (VBTS) are essential for achieving dexterous robotic manipulation, yet the tactile sim-to-real gap remains a fundamental bottleneck. Current tactile simulations suffer from a persistent dilemma: simplified geometric projections lack physical authenticity, while high-fidelity Finite Element Methods (FEM) are too computationally prohibitive for large-scale reinforcement learning. In this work, we present Tacmap, a high-fidelity, computationally efficient tactile simulation framework anchored in volumetric penetration depth. Our key insight is to bridge the tactile sim-to-real gap by unifying both domains through a shared deform map representation. Specifically, we compute 3D intersection volumes as depth maps in simulation, while in the real world, we employ an automated data-collection rig to learn a robust mapping from raw tactile images to ground-truth depth maps. By aligning simulation and real-world in this unified geometric space, Tacmap minimizes domain shift while maintaining physical consistency. Quantitative evaluations across diverse contact scenarios demonstrate that Tacmap's deform maps closely mirror real-world measurements. Moreover, we validate the utility of Tacmap through an in-hand rotation task, where a policy trained exclusively in simulation achieves zero-shot transfer to a physical robot.

翻译：基于视觉的触觉传感器是实现灵巧机器人操作的关键，但触觉模拟到现实的差距仍是根本性瓶颈。当前的触觉模拟面临持续性困境：简化的几何投影缺乏物理真实性，而高保真有限元方法的计算开销过大，难以支持大规模强化学习。在本工作中，我们提出Tacmap——一种基于体积穿透深度的高保真、计算高效的触觉模拟框架。我们的核心见解是通过共享形变图表示统一两个域，从而弥合触觉模拟与现实差距。具体而言，我们在仿真中计算三维交集体积作为深度图，而在真实世界中采用自动化数据采集装置学习从原始触觉图像到真实深度图的鲁棒映射。通过在这一统一几何空间中对齐仿真与现实，Tacmap在保持物理一致性的同时最小化域偏移。跨多种接触场景的定量评估表明，Tacmap的形变图能够紧密复现真实世界测量结果。此外，我们通过手内旋转任务验证了Tacmap的实用性——仅基于仿真训练的策略可直接零样本迁移至实体机器人。