Our paper, “Deep Neural Network Based Electrical Impedance Tomographic Sensing Methodology for Large-Area Robotic Tactile Sensing” has been published in IEEE Transactions on Robotics (T-RO).

Authors: Hyunkyu Park, Kyungseo Park, Sangwoo Mo, and Jung Kim
Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance.