Generalizable algorithms for tactile sensing remain underexplored, primarily due to the diversity of sensor modalities. Recently, many methods for cross-sensor transfer between optical (vision-based) tactile sensors have been investigated, yet little work focus on non-optical tactile sensors. To address this gap, we propose an encoder-decoder architecture to unify tactile data across non-vision-based sensors. By leveraging sensor-specific encoders, the framework creates a latent space that is sensor-agnostic, enabling cross-sensor data transfer with low errors and direct use in downstream applications. We leverage this network to unify tactile data from two commercial tactile sensors: the Xela uSkin uSPa 46 and the Contactile PapillArray. Both were mounted on a UR5e robotic arm, performing force-controlled pressing sequences against distinct object shapes (circular, square, and hexagonal prisms) and two materials (rigid PLA and flexible TPU). Another more complex unseen object was also included to investigate the model’s generalization capabilities. We show that alignment in latent space can be implicitly learned from joint autoencoder training with matching contacts collected via different sensors. We further demonstrate the practical utility of our approach through contact geometry estimation, where downstream models trained on one sensor’s latent representation can be directly applied to another without retraining.

The adoption of tactile sensors in robotics is hindered by their high cost and fragility. We designed and validated a cost-effective and robust barometric tactile sensor array, whose material cost is below 80 USD. Unlike past work, we do not mold the rubber surface over the barometers but instead keep it as a separate element, leading to a design that is easy to fabricate and repair. Machine learning techniques are applied to enhance the sensor's localization precision, increasing the effective resolution from 6 mm (the distance between adjacent barometers) to 0.284 mm. To investigate the localization model's robustness, we utilized anE-TRollrobotic gripper to roll differently shaped prismatic objects across the sensing surface mounted on one finger. Under these uncontrolled settings, we achieved a satisfactory average real-time localization resolution of within 2.66 mm. Furthermore, we demonstrate a novel practical application: The E-TRoll mimics a one-DoF parallel gripper inferring a cube's orientation relative to the sensor. The range of orientations is split into four classes, which a trained CNN-LSTM model can predict with an 86.91% five-fold cross-validated accuracy.