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.
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