跨传感器零样本迁移力图估计，实现不同设备间通用建模。
Zero-Shot Transfer of Force Map Estimation Across GelSight Mini Sensors
- 用UniT模型重建通用触觉图像，实现跨设备输入对齐。
- 力图估计阶段平均误差仅1.13N，SSIM达0.934，性能稳定。
- 适用于工业场景中多传感器快速部署，无需重新训练。
尽管触觉传感器制造已趋于工业化，但多数仍由科研实验室手工制作，导致性能难以标准化，需为每台设备重复采集数据并训练模型。为解决此问题，本文提出一种可在不同GelSight Mini传感器间实现3D力图估计零样本迁移的方法。该方法分两步：第一阶段采用基于UniT的模型将输入触觉图像重建为通用触觉图像；第二阶段使用U-Net网络估计3D力图。实验结果显示，图像重建阶段的结构相似性（SSIM）达到0.9338 ± 0.0358，力估计阶段的平均绝对误差（MAE_F）为1.1294 ± 1.5934(N)，表现优异。
原文摘要 · Abstract (English)
Despite the rapid industrialization of the touch sensor manufacturing process, most of these sensors are still handmade in research laboratories. This complicates standardizing their performance, requiring the repetition of data collection and training models for each unit produced. To address this problem, this paper presents a method that can generalize the estimation of 3D force maps across different GelSight Mini sensor units, regardless of the sensor version. Specifically, the method consists of two stages: a domain adaptation stage, in which the input tactile image is reconstructed as a general tactile image using a UniT-based model; and a stage for estimating 3D force maps employing a U-Net network. Our proposal achieves promising results in both steps, such as an SSIM of 0.9338 +- 0.0358 in the image reconstruction phase and an MAE_F of 1.1294 +- 1.5934(N) in the force estimation phase.
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