Contact often occurs without macroscopic surface deformation, such as during interaction with liquids, semi-liquids, or ultra-soft materials. However, most existing tactile sensors rely on deformation to infer contact, making such light-contact interactions difficult to perceive robustly. To address this, we present LightTact, a visual-tactile fingertip sensor that makes contact directly visible via a deformation-independent principle. LightTact features an ambient-blocking optical configuration that suppresses both external light and internal illumination at non-contact regions, while transmitting only the scattered light generated at true contacts. As a result, LightTact produces high-contrast raw images in which non-contact pixels remain near-black (mean gray value < 3) and contact pixels preserve the natural appearance of the contacting surface. Built on this, LightTact achieves accurate pixel-level contact segmentation that is robust to material properties, contact force, surface appearance, and environmental lighting. We further demonstrate that LightTact unlocks new robotic manipulation behaviors that require detection of extremely light contact, including water spreading, facial-cream dipping, and soft thin-film interaction. In addition, we show that LightTact's spatially aligned visual-tactile images can be directly interpreted by vision-language models.

翻译：接触通常发生在没有宏观表面变形的情况下，例如与液体、半液体或超软材料交互时。然而，大多数现有触觉传感器依赖变形推断接触，这使得此类轻接触交互难以被鲁棒感知。为解决这一问题，我们提出LightTact，一种基于变形无关原理、通过直接可视化接触的视触觉指尖传感器。LightTact采用环境光阻断光学配置，抑制非接触区域的外部光线与内部照明，仅透射真实接触产生的散射光。因此，LightTact生成高对比度原始图像，其中非接触像素保持近黑色（平均灰度值<3），接触像素保留接触表面的自然外观。在此基础上，LightTact实现像素级精确接触分割，且对材料属性、接触力、表面外观和环境光照均具有鲁棒性。我们进一步证明，LightTact可解锁需要检测极轻接触的机器人操作行为，包括水扩散、面霜蘸取和软薄膜交互。此外，我们展示了LightTact空间对齐的视触觉图像可被视觉语言模型直接解读。