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.

The 2018 IROS Fan Robotic Challenge tasked participants with programming a robot to autonomously open and close a Spanish folding fan, highlighting the obstacles still associated with the dexterous manipulation of objects for robotic systems. Since high DoFs grippers are complex to coordinate and overkill for many industrial processes, our approach used an under-actuated parallel gripper with a 3D-printed adaptation to precisely grasp the fan in such a manner that gravity could be leveraged to act on the fan to produce an extrinsic, or external, dexterity. With our approach, we completed the challenge in 12.38 seconds, resulting in a top three finish. Furthermore, using a multi-modal tactile sensor, we analyzed the vibrations in the grasp during the manipulation and were able to distinguish the opening and closing of the fan from the motion of the robot with a 83% accuracy.

The growing demand in industry for robots capable of performing a variety of tasks requires an increased capability in robotic grasping. Humans are adept at interacting with novel objects, a skill attributed primarily to tactile feedback in the form of exteroception and proprioception. This paper presents a novel way to incorporate exteroception and proprioception into grasp stability assessment: by using convolutional neural networks. This method improves upon the results of a unsupervised feature learning approach that used similar tactile feedback. 1000 different grasps on 100 objects were used to train and test the network. The network achieved an overall accuracy of 88.4% while predicting the failure class with an accuracy of 92.7%.