用视觉数据提升磁性触觉传感器的分辨率，实现高精度形变重建。
SuperMag: Vision-based Tactile Data Guided High-resolution Tactile Shape Reconstruction for Magnetic Tactile Sensors
- 利用视觉触觉数据监督磁性传感器的超分辨率重建。
- 实现125Hz采样率下2.5ms内完成高分辨率形变推断。
- 适合需要高精度触觉感知的机器人应用场景。
基于磁性触觉传感器（MBTS）兼具紧凑设计与高频操作的优点，但受限于稀疏的税点阵列导致空间分辨率低。本文提出SuperMag，通过高分辨率视觉触觉传感器（VBTS）数据指导MBTS的超分辨率形变重建。设计并开源了具有相同接触模块的对称式同步采集系统，实现视觉与磁信号的同步获取。将形变重建建模为条件生成问题，采用条件变分自编码器从低分辨率MBTS输入中推断出高分辨率形状。MBTS采样频率达125 Hz，形变重建推理时间保持在2.5 ms以内。该跨模态协同显著提升了MBTS的触觉感知能力，有望推动其在高精度机器人任务中的应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。