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.

翻译：触觉传感器在过去已被用于力估计，特别是基于视觉的触觉传感器（VBTS）因其高空间分辨率和低成本，近年来已成为一种新趋势。在本工作中，我们设计并实现了多种方法，利用从VBTS获得的不同类型的无标记视觉触觉表征来估计法向抓取力。我们的主要目标是通过在机器人抓取任务中的性能分析，确定最合适的视觉触觉表征。我们的方案已在用我们的DIGIT传感器生成的数据集以及从另一项先进工作中获得的GelSight Mini传感器数据上进行了测试。我们还测试了我们最佳方法（称为RGBmod）的泛化能力。结果得出两个主要结论。首先，RGB视觉触觉表征是比深度图像或两者组合更好的输入选项，用于估计法向抓取力。其次，RGBmod在真实场景中对10个未见过的日常物体进行测试时表现良好，平均相对误差为0.125 ± 0.153。此外，我们表明，在相同任务中使用RGB和深度信息的文献中，我们的方案优于其他工作。