The use of data-driven techniques for tactile data processing and classification has recently increased. However, collecting tactile data is a time-expensive and sensor-specific procedure. Indeed, due to the lack of hardware standards in tactile sensing, data is required to be collected for each different sensor. This paper considers the problem of learning the mapping between two tactile sensor outputs with respect to the same physical stimulus -- we refer to this problem as touch-to-touch translation. In this respect, we proposed two data-driven approaches to address this task and we compared their performance. The first one exploits a generative model developed for image-to-image translation and adapted for this context. The second one uses a ResNet model trained to perform a regression task. We validated both methods using two completely different tactile sensors -- a camera-based, Digit and a capacitance-based, CySkin. In particular, we used Digit images to generate the corresponding CySkin data. We trained the models on a set of tactile features that can be found in common larger objects and we performed the testing on a previously unseen set of data. Experimental results show the possibility of translating Digit images into the CySkin output by preserving the contact shape and with an error of 15.18% in the magnitude of the sensor responses.

翻译：近年来，数据驱动技术在触觉数据处理与分类中的应用日益增多。然而，触觉数据的采集是一个耗时且传感器依赖的过程。由于触觉传感领域缺乏硬件标准，每种不同传感器都需要独立采集数据。本文研究针对同一物理刺激，学习两种触觉传感器输出之间映射关系的问题——我们将其定义为触觉-触觉转换。为此，我们提出了两种数据驱动方法来解决该任务，并对其性能进行了比较。第一种方法采用为图像-图像转换开发的生成模型，并针对本场景进行适配；第二种方法使用经过训练执行回归任务的ResNet模型。我们使用两种完全不同的触觉传感器——基于视觉的Digit传感器与基于电容的CySkin传感器——对两种方法进行了验证。具体而言，我们利用Digit图像生成对应的CySkin数据。模型训练采用常见大型物体所包含的触觉特征数据集，并在未见数据集上进行测试。实验结果表明，通过保持接触形状特征，可将Digit图像转换为CySkin输出，其传感器响应幅值的转换误差为15.18%。