LightTact: A Visual-Tactile Fingertip Sensor for Deformation-Independent Contact Sensing

TL;DR

LightTact is a novel visual-tactile sensor that detects contact independently of deformation, enabling robust perception of light contacts and facilitating advanced manipulation tasks.

Contribution

We introduce LightTact, a contact visualization sensor that operates independently of surface deformation, improving contact detection in soft and liquid interactions.

Findings

High-contrast contact images with minimal ambient light interference

Robust pixel-level contact segmentation across various materials and conditions

Enables new manipulation behaviors like water spreading and soft surface interaction

Abstract

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.