Training machine learning models for robotic tactile sensing requires vast amounts of data, yet obtaining realistic interaction data remains a challenge due to physical complexity and variability. Simulating tactile sensors is thus a crucial step in accelerating progress. This paper presents SPLIT, a novel method for simulating image-based tactile sensors, with a primary focus on the DIGIT sensor. Central to our approach is a latent space arithmetic strategy that explicitly disentangles contact geometry from sensor-specific optical properties. Unlike methods that require recalibration for every new unit, this disentanglement allows SPLIT to adapt to diverse DIGIT backgrounds and even transfer data to distinct sensors like the GelSight R1.5 without full model retraining. Beyond this adaptability, our approach achieves faster inference speeds than existing alternatives. Furthermore, we provide a calibrated finite element method (FEM) soft-body mesh simulation with variable resolution, offering a tunable trade-off between speed and fidelity. Additionally, our algorithm supports bidirectional simulation, allowing for both the generation of realistic images from deformation meshes and the reconstruction of meshes from tactile images. This versatility makes SPLIT a valuable tool for accelerating progress in robotic tactile sensing research.

翻译：训练用于机器人触觉传感的机器学习模型需要大量数据，但由于物理复杂性和可变性，获取真实的交互数据仍面临挑战。因此，模拟触觉传感器是加速该领域进展的关键步骤。本文提出了SPLIT，一种用于模拟基于图像的触觉传感器的新方法，主要聚焦于DIGIT传感器。该方法的核心是一种隐式空间算术策略，能够显式地将接触几何形状与传感器特定的光学属性解耦。与每台新设备都需要重新校准的方法不同，这种解耦能力使SPLIT能够适应不同的DIGIT背景，甚至无需完整模型重训练即可将数据迁移至GelSight R1.5等不同传感器。除适应性外，本方法的推理速度也优于现有替代方案。此外，我们提供了一种带可变分辨率的校准有限元法软体网格仿真，可在速度与保真度之间实现可调节的权衡。该算法还支持双向仿真，既能从形变网格生成逼真图像，也能从触觉图像重建网格。这种多功能性使SPLIT成为加速机器人触觉传感研究的有效工具。