AI 中文总结
该研究针对GelSight Mini传感器性能难标准化、需重复训练的问题，提出含UniT图像重建与U-Net力图估计两阶段的方法，实现不同传感器间零样本力图迁移，取得良好实验结果。
AI 中文摘要
尽管触觉传感器制造流程已实现快速工业化，但多数此类传感器仍由研究实验室手工制作，这使其性能标准化变得复杂，需为每个生产单元重复数据收集与模型训练。为解决该问题，本文提出一种可在不同GelSight Mini传感器单元间（无论版本如何）推广3D力图估计的方法。该方法分为两个阶段：领域适应阶段，使用基于UniT的模型将输入触觉图像重建为通用触觉图像；3D力图估计阶段，采用U-Net网络完成。本方案在两个阶段均取得良好结果，如图像重建阶段SSIM为0.9338±0.0358，力估计阶段MAE_F为1.1294±1.5934(N)。
英文摘要
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.
CommentsAccepted for publication in IEEE Sensors Letter
