不用标记的触觉传感器，用图像估抓握力更准
Grasping Force Estimation for Markerless Visuotactile Sensors
- 用无标记视觉触觉数据表征抓握力，选了三种输入方式比对
- RGB图像表现最优，平均相对误差仅0.125±0.153
- 新方法在10个未知日常物体上表现稳定，适合实际机器人应用
触觉传感器常用于力估计，尤其是基于视觉的触觉传感器（VBTS）因高空间分辨率和低成本成为新趋势。本文设计并实现多种方法，利用不同类型的无标记视觉触觉表征来估计法向抓握力。目标是通过机器人抓取任务中的性能分析，确定最合适的表征方式。实验使用自研DIGIT传感器生成的数据集，以及另一项先进工作使用的GelSight Mini传感器数据集。还测试了最佳方法RGBmod的泛化能力。结果表明：第一，与深度图或二者结合相比，仅用RGB视觉触觉表征更优；第二，RGBmod在10个未见过的日常物体的真实场景中测试，平均相对误差为0.125±0.153。此外，该方法在同类任务中优于已有文献中的方法。
原文摘要 · Abstract (English)
Tactile sensors have been used for force estimation in the past, especially Vision-Based Tactile Sensors (VBTS) have recently become a new trend due to their high spatial resolution and low cost. In this work, we have designed and implemented several approaches to estimate the normal grasping force using different types of markerless visuotactile representations obtained from VBTS. Our main goal is to determine the most appropriate visuotactile representation, based on a performance analysis during robotic grasping tasks. Our proposal has been tested on the dataset generated with our DIGIT sensors and another one obtained using GelSight Mini sensors from another state-of-the-art work. We have also tested the generalization capabilities of our best approach, called RGBmod. The results led to two main conclusions. First, the RGB visuotactile representation is a better input option than the depth image or a combination of the two for estimating normal grasping forces. Second, RGBmod achieved a good performance when tested on 10 unseen everyday objects in real-world scenarios, achieving an average relative error of 0.125 +- 0.153. Furthermore, we show that our proposal outperforms other works in the literature that use RGB and depth information for the same task.
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