# Cluster Haptic Texture Dataset: Haptic Texture Dataset with Varied Velocity-Direction Sliding Contacts

arXiv: https://arxiv.org/abs/2407.16206 · HTML: https://mlsanity.com/paper/2407.16206/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [Tactile Data Recording System for Clothing with Motion-Controlled Robotic Sliding](https://mlsanity.com/paper/2511.11634/) (2025)
- [When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2603.25115/) (2026)
- [A 3D-Printable Dataset for Fair Testing and Comparisons of Tactile Sensors](https://mlsanity.com/paper/2606.25886/) (2026)
- [Beyond Multimodal Alignment: Certifying Physical Language through Response Substitution and Ordered Execution](https://mlsanity.com/paper/2608.19492/) (2026)

## Abstract (arXiv)

Haptic sciences and technologies benefit greatly from comprehensive datasets that capture tactile stimuli under controlled, systematic conditions. However, existing haptic datasets collect data through uncontrolled exploration, which hinders the systematic analysis of how motion parameters (e.g., motion direction and velocity) influence tactile perception. This paper introduces Cluster Haptic Texture Dataset, a multimodal dataset recorded using a 3-axis machine with an artificial finger to precisely control sliding velocity and direction. The dataset encompasses 118 textured surfaces across 9 material categories, with recordings at 5 velocity levels (20-60 mm/s) and 8 directions. Each surface was tested under 160 conditions, yielding 18,880 synchronized recordings of audio, acceleration, force, position, and visual data. Validation using convolutional neural networks demonstrates classification accuracies of 96% for texture recognition, 88.76% for velocity estimation, and 78.79% for direction estimation, confirming the dataset's utility for machine learning applications. This resource enables research in haptic rendering, texture recognition algorithms, and human tactile perception mechanisms, supporting the development of realistic haptic interfaces for virtual reality systems and robotic applications.