The ability to associate touch with other modalities has huge implications for humans and
computational systems. However multimodal learning with touch remains challenging due to …

Recently, several morphologies, each with its advantages, have been proposed for the\textit
{GelSight} high-resolution tactile sensors. However, existing simulation methods are limited …

Generating tactile data (eg, friction coefficient) from audio and visual modalities can avoid
time-consuming practical measurements and ensure high-fidelity haptic rendering of surface …

Deep generative models have various content creation applications such as graphic design,
e-commerce, and virtual try-on. However, current works mainly focus on synthesizing …

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The
membrane of these sensors can be painted with markers or remain markerless, enabling …

Tactile sensing plays an irreplaceable role in robotic material recognition. It enables robots
to distinguish material properties such as their local geometry and textures, especially for …

Cross-Modal Retrieval (CMR), which retrieves relevant items from one modality (eg, audio)
given a query in another modality (eg, visual), has undergone significant advancements in …

Vibration acceleration signals allow humans to perceive the surface characteristics of
textures during tool-surface interactions. However, acquiring acceleration signals requires a …

Haptic feedback is essential for humans to successfully perform complex and delicate
manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense …

Cross-Modal Retrieval (CMR), which retrieves relevant items from one modality (eg, audio)
given a query in another modality (eg, visual), has undergone significant advancements in …