TensorTouch: Calibration of Tactile Sensors for High Resolution Stress Tensor and Deformation for Dexterous Manipulation

Advanced dexterous manipulation requires identifying and controlling multiple simultaneous contacts, including interactions with compliant objects where deformations are large. Raw optical tactile images are information rich but lack calibrated physical meaning, limiting cross-sensor use and real-world deployment. In this article, we present TensorTouch, a calibration framework that combines finite element analysis and learning to infer dense deformation and stress/force fields from a single tactile image. Across real sensors, TensorTouch achieves contact localization errors under 1.29 mm and mean force errors under 0.139 N per axis. In a multiobject selective grasp task with two simultaneously contacted objects (including identical cables), the system achieves up to 90.0% success. We further demonstrate robustness under repeated loading, yielding 38.295 dB peak signal-to-noise ratio between the initial tactile image and the image after 20 000 contacts. The learned model runs in real time at 95 Hz on an RTX 5090, proving to be suitable for contact-rich, dexterous manipulation.