SuperMag: Vision-based Tactile Data Guided High-resolution Tactile Shape Reconstruction for Magnetic Tactile Sensors
Peiyao Hou, Danning Sun, Meng Wang, Yuzhe Huang, Zeyu Zhang, Hangxin Liu, Wanlin Li, Ziyuan Jiao
Abstract
Magnetic-based tactile sensors (MBTS) combine the advantages of compact design and high-frequency operation but suffer from limited spatial resolution due to their sparse taxel arrays. This paper proposes SuperMag, a tactile shape reconstruction method that addresses this limitation by leveraging high-resolution vision-based tactile sensor (VBTS) data to supervise MBTS super-resolution. Co-designed, open-source VBTS and MBTS with identical contact modules enable synchronized data collection of high-resolution shapes and magnetic signals via a symmetric calibration setup. We frame tactile shape reconstruction as a conditional generative problem, employing a conditional variational auto-encoder to infer high-resolution shapes from low-resolution MBTS inputs. The MBTS achieves a sampling frequency of 125 Hz, whereas the shape reconstruction sustains an inference time within 2.5 ms. This cross-modality synergy advances tactile perception of the MBTS, potentially unlocking its new capabilities in high-precision robotic tasks.
BibTeX
@inproceedings{iros2025_supermagvisionba,
title = {SuperMag: Vision-based Tactile Data Guided High-resolution Tactile Shape Reconstruction for Magnetic Tactile Sensors},
author = {Peiyao Hou and Danning Sun and Meng Wang and Yuzhe Huang and Zeyu Zhang and Hangxin Liu and Wanlin Li and Ziyuan Jiao},
booktitle = {IROS 2025},
year = {2025}
}