Deep Neural Network Approach in Electrical Impedance Tomographybased Realtime Soft Tactile Sensor Recently, a wholebody tactile sensing have emerged in robotics for safe humanrobot interaction. A key issue in the wholebody tactile sensing is ensuring largearea manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in largearea tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwellxe2x80x99s equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EITbased tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabricbased soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.

Transfer learning for visionbased tactile sensing Due to the complexity of modeling the elastic properties of materials, the use of machine learning algorithms is continuously increasing for tactile sensing applications. Recent advances in deep neural networks applied to computer vision make visionbased tactile sensors very appealing for their highresolution and low cost. A soft optical tactile sensor that is scalable to large surfaces with arbitrary shape is discussed in this paper. A supervised learning algorithm trains a model that is able to reconstruct the normal force distribution on the sensorxe2x80x99s surface, purely from the images recorded by an internal camera. In order to reduce the training times and the need for large datasets, a calibration procedure is proposed to transfer the acquired knowledge across multiple sensors while maintaining satisfactory performance.