As robots move toward more complex environments, imbuing them with a sense of touch similar to humans becomes increasingly important. To fulfill that goal, there has been significant research conducted in the past few decades to develop a tactile sensor that matches human level touch capabilities. Recently, the progress in capacitive touch screens has made capacitive sensing a very appealing option for such a sensor, and therefore many research groups have proposed novel designs of tactile sensors based on capacitive technologies. This technology has the advantage of generating a predictable sensor response with a high degree of sensitivity, but has the drawback of a limited spatial resolution. This paper shows how using mutual capacitance in combination with a microstructured dielectric can lead to a very sensitive sensor that also possesses a high spatial resolution. The response of the sensor in relation to its various components is explored in order to fully comprehend the physical principles of the sensing mechanism and generate a predictable output.

Research around tactile sensing for grasp stability prediction in robotic manipulators continues to be popular, however few works are able to achieve a high classification accuracy. Due to simulation complexity, data-driven methods are often forced to rely on experimental data, yielding small, often unbalanced, data sets. In this work, the authors use a 3972 sample data set to explore the effects of the data set composition on the performance of a classifier. While maintaining a similar overall accuracy, the ability to recognize a grasp failure was significantly impacted by the composition of the data set. The authors propose an autonomous pipeline designed to generate more diverse failure grasps. On failure-rich data, a tactile-based classifier with a balanced training set achieved a classification accuracy of 84.68% while maintaining a recall of the grasp failure class of 76%. This represents a 71.79% improvement in recall over a model trained on a larger but unbalanced data set.