跨传感器触觉数据翻译，让不同触觉设备输出相互转换。
Touch-to-Touch Translation -- Learning the Mapping Between Heterogeneous Tactile Sensing Technologies
- 用生成模型和回归网络学习两种触觉传感器间的映射关系。
- 将Digit图像转为CySkin数据，响应幅度误差仅15.18%。
- 适合触觉系统集成与多传感器数据融合的研究者参考。
基于数据驱动的触觉数据处理与分类方法近年来日益普及，但触觉数据采集耗时且依赖特定传感器。由于触觉传感缺乏硬件标准，每种传感器需单独采集数据。本文研究同一物理刺激下两种不同触觉传感器输出之间的映射问题，即“触觉到触觉翻译”。为此提出两种数据驱动方法：一是适配于图像到图像翻译的生成模型；二是用于回归任务的ResNet模型。在两种完全不同的触觉传感器——基于相机的Digit与基于电容的CySkin上验证。以Digit图像生成对应CySkin数据，训练使用常见大物体的触觉特征，测试集为未见数据。实验结果表明，可实现从Digit图像到CySkin输出的有效转换，保留接触形状，传感器响应幅度误差为15.18%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。