用一张参考图和物理参数生成逼真触觉图像，解决数据难收集问题
ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image
- 输入单张触觉图+接触力和位置，生成多样且真实的触觉图像
- 在3个下游任务中提升性能，真实场景实验验证有效性
- 适合需要高质量触觉数据的机器人感知与操作研究
基于视觉的触觉感知广泛应用于感知、重建和机器人操作。然而，由于传感器与物体交互具有局部性且传感器间存在不一致性，大规模触觉数据采集成本高昂。现有数据扩展方法如仿真和自由生成，常导致输出不真实且下游任务迁移性差。为此，我们提出ControlTac，一种两阶段可控框架，仅需一张参考触觉图像、接触力和接触位置即可生成符合物理规律的多样化触觉图像。通过三个下游任务实验，证明该方法能有效增强触觉数据集并带来稳定性能提升。三项真实世界实验进一步验证了其实际应用价值。
原文摘要 · Abstract (English)
Vision-based tactile sensing has been widely used in perception, reconstruction, and robotic manipulation. However, collecting large-scale tactile data remains costly due to the localized nature of sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data, such as simulation and free-form tactile generation, often suffer from unrealistic output and poor transferability to downstream tasks. To address this, we propose ControlTac, a two-stage controllable framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact position. With those physical priors as control input, ControlTac generates physically plausible and varied tactile images that can be used for effective data augmentation. Through experiments on three downstream tasks, we demonstrate that ControlTac can effectively augment tactile datasets and lead to consistent gains. Our three real-world experiments further validate the practical utility of our approach. Project page: https://dongyuluo.github.io/controltac.
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