Good learning results can be caused by well-trained learning models using sensor datasets with consistent signal patterns and ranges. However, since soft sensors have manufacturing tolerances for several reasons, such as variations of elastomer properties and manufacturing human errors, even homogeneous sensors have variations of characteristics, resulting in performance variations, such as different initial offset and operating ranges. In addition, test conditions such as the size of an indenter and clamping types can make a sensor behave differently; output data can be susceptive to change even if input data are the same. In this case, even if a model shows excellent learning results based on datasets from one specific sensor, the model cannot be applied to other sensors. In addition, since sensors made of soft materials are not durable enough for long time usage, drift can occur in sensor response as sensor structure is permanently deformed. Although there are some learning approaches such as transfer learning [53, 54] and multi-domain learning to address such limitations, improvements in sensor hardware aspects of sensing mechanisms, materials, and manufacturing processes must be accompanied for fundamental solutions.

Machine learning has its ability to extract important features from massive and multi-dimensional data. This enables researchers to design new types of soft sensors based on novel mechanisms while minimizing the concerns of dealing with sensor behaviors that can be difficult to analyze using analytical models. There are typical examples such as a multi-axis force sensor using a silicone matrix embedding multiple biometers [52] and tactile sensors capable of detecting contact forces and shape of contact objects by analyzing silicone surface images using camera sensors [53, 56, 60]. Since these novel sensors have a hardware design or a sensing mechanism that makes sensing datasets more complicated, they cannot be easily developed due to the limitation of data processing methods until activation of the use of machine learning techniques. Therefore, by understanding the characteristics of various learning approaches and taking advantage of an appropriate machine learning technique, researchers can try more various sensor designs and mechanisms to develop novel sensing structures without concerns about data processing. This shows one of the technical synergies that the learning-based approaches and soft sensors can create in the future.

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