Contact often occurs without macroscopic surface deformation, such as during interaction with liquids, semi-liquids, or ultra-soft materials. 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, optics-based principle. LightTact uses an ambient-blocking optical configuration that suppresses both external light and internal illumination at non-contact regions, while transmitting only the diffuse 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 integrate LightTact on a robotic arm and demonstrate manipulation behaviors driven by extremely light contact, including water spreading, facial-cream dipping, and thin-film interaction. Finally, we show that LightTact's spatially aligned visual-tactile images can be directly interpreted by existing vision-language models, enabling resistor value reasoning for robotic sorting.

翻译：接触通常发生在没有宏观表面形变的情况下，例如与液体、半液体或超软材料相互作用时。大多数现有触觉传感器依赖形变来推断接触，这使得此类轻接触交互难以被鲁棒地感知。为解决这一问题，我们提出了LightTact，一种视觉-触觉指尖传感器，它通过一种与形变无关、基于光学的原理使接触直接可见。LightTact采用了一种环境光阻隔光学配置，该配置既能抑制外部光线，也能抑制非接触区域的内部照明，同时仅传输真实接触处产生的漫射光。因此，LightTact生成高对比度的原始图像，其中非接触像素保持近黑色（平均灰度值 < 3），而接触像素则保留了接触表面的自然外观。基于此，LightTact实现了精确的像素级接触分割，且对材料特性、接触力、表面外观和环境光照具有鲁棒性。我们进一步将LightTact集成到机械臂上，并展示了由极轻接触驱动的操控行为，包括水铺展、面霜蘸取和薄膜交互。最后，我们证明LightTact空间对齐的视觉-触觉图像可以被现有的视觉-语言模型直接解读，从而实现了用于机器人分拣的电阻值推理。