A 3D-Printable Dataset for Fair Testing and Comparisons of Tactile Sensors

2026-06-24

AI Summary

This work addresses the limitation of existing tactile texture datasets, which rely on proprietary sensors and hinder fair cross-device comparison and reproducible research. The authors propose the first open-source, physically reproducible 3D-printed tactile texture benchmark, comprising six parametric surface patterns derived from sine waves and Fourier functions. Manufacturing consistency is validated across multiple 3D printers and filament materials. Experimental evaluation using optical TacTip imaging combined with neural network and PCA-based classification models demonstrates that high-precision printing significantly enhances texture consistency and enables strong within-device generalization. Although cross-device generalization remains constrained by geometric discrepancies among sensors, this benchmark establishes a foundational framework for standardized evaluation of tactile sensing systems.

Abstract

Existing texture datasets for tactile sensing primarily consist of sensor readings from a specific sensor interacting with available surfaces/objects rather than describing the textures themselves, limiting fair comparison between tactile sensors and hindering reproducible research. In this work, we introduce a 3D-printable dataset of mathematically defined textures designed to be fabricated reliably across different printers and filament types. The dataset consists of six parametrically generated surface patterns derived from combinations of sine-wave and Fourier-based functions, giving controlled variation in spatial frequency, amplitude, and directional structure. We evaluate the reproducibility of these textures across three popular 3D printers and multiple filament types by measuring variance in images captured using an optical TacTip sensor under controlled contact conditions. Our results show that print quality, particularly peak sharpness and stringing, affects tactile variance, with higher-end printers producing significantly more consistent signatures. Classification experiments using neural networks and PCA-based models further demonstrate that high-quality prints support strong within-printer generalisation, while cross-printer generalisation remains challenging due to geometric inconsistencies. This work establishes the first openly available, physically reproducible 3D-printed texture benchmark, providing a foundation for fair comparison of tactile sensors.

Problem

• tactile sensors
• texture dataset
• fair comparison
• reproducible research
• 3D printing

Innovation

• 3D-printable textures
• tactile sensor benchmarking
• reproducible tactile dataset
• parametric surface patterns
• cross-printer generalization