Touch with Insight: Physics-Aware Data-Driven Learning for EIT-Based Tactile Sensing

Abstract

Tactile sensing is essential for enabling dexterous robotic manipulation, yet estimating contact states such as location and force from high-dimensional sensor measurements remains challenging due to noise and complex nonlinear mappings between raw signals and physical interaction states. In this work, we propose a physics-informed contact modeling framework that combines the flexibility of deep models with inductive biases from physical modeling. Focusing on electrical impedance tomography (EIT) tactile skins, our approach incorporates knowledge of the EIT forward model by regularizing neural estimators with a latent-space consistency constraint, stabilizing the ill-posed inverse mapping from voltages to contact states. To support robust training and evaluation, we also develop a high-fidelity simulation pipeline that incorporates key hardware imperfections to better bridge the sim-to-real gap. We benchmark multiple architectures—including multilayer perceptrons, convolutional networks, Transformer-based models, and autoencoder regressors—on both real and synthetic datasets. Results show that the proposed hybrid approach consistently improves estimation accuracy, particularly for force prediction, and generalizes across domains. These findings highlight the value of embedding physical priors into learning pipelines for reliable tactile state estimation in robotic manipulation.