A 3D-Printable Dataset for Fair Testing and Comparisons of Tactile Sensors
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 repro...
| Authors | Dexter R. Shepherd, Nicolas Herzig, Phil Husbands et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 June 2026 |
| Last updated at source | 22 September 2026 |
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