  Human perception integrates multisensory information, with tactile perception
playing a key role in object and surface recognition. While human-machine
interfaces with haptic modalities offer enhanced system performance, existing
datasets focus primarily on visual data, overlooking comprehensive haptic
information. Previous haptic texture databases have recorded sound and
acceleration signals, but often ignore the nuanced differences between
probe-texture and finger-texture interactions. Recognizing this shortcoming, we
present the Cluster Haptic Texture Database, a multimodal dataset that records
visual, auditory, and haptic signals from an artificial urethane rubber
fingertip interacting with different textured surfaces. This database, designed
to mimic the properties of the human finger, includes five velocity levels and
eight directional variations, providing a comprehensive study of tactile
interactions. Our evaluations reveal the effectiveness of classifiers trained
on this dataset in identifying surfaces, and the subtleties of estimating
velocity and direction for each surface.
