SPLIT可分离触觉传感器的物理接触与光学特性，实现跨设备快速模拟。
SPLIT: Separating Physical-Contact via Latent Arithmetic in Image-Based Tactile Sensors
- 通过潜空间算术显式解耦接触几何与传感器光学属性。
- 支持不同背景和传感器间迁移，无需重新训练模型。
- 兼具双向仿真能力，适合触觉感知研究者使用。
机器人触觉感知的机器学习模型训练需要大量数据，但真实交互数据因物理复杂性和变异性难以获取。因此，模拟触觉传感器成为加速进展的关键。本文提出SPLIT方法，用于图像式触觉传感器（以DIGIT传感器为主）的模拟。其核心是潜空间算术策略，显式解耦接触几何与传感器特有光学属性。相比需为每台新设备重新校准的方法，SPLIT可适应不同DIGIT背景，甚至将数据迁移到如GelSight R1.5等不同传感器，且无需完整重训练。此外，该方法推理速度优于现有方案。我们还提供可调分辨率的有限元法（FEM）软体网格模拟，实现速度与保真度的权衡。算法支持双向模拟：既可从形变网格生成真实图像，也可从触觉图像重建网格。SPLIT为机器人触觉感知研究提供了高效工具。
原文摘要 · Abstract (English)
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.
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