Efficient Tactile Perception with Soft Electrical Impedance Tomography and Pre-trained Transformer

TL;DR

This paper introduces PTET, a learning-based framework that significantly improves tactile reconstruction in robotics by reducing data requirements and bridging the simulation-reality gap using pre-trained transformers.

Contribution

It presents a novel pre-trained transformer approach for EIT-based tactile sensing that requires fewer annotated samples and enhances reconstruction accuracy in real-world robotic applications.

Findings

Requires 99.44% fewer annotated samples than previous methods.

Achieves up to 43.57% improvement in reconstruction performance.

Demonstrates superior real-world tactile sensing and object handling capabilities.

Abstract

Tactile sensing is fundamental to robotic systems, enabling interactions through physical contact in multiple tasks. Despite its importance, achieving high-resolution, large-area tactile sensing remains challenging. Electrical Impedance Tomography (EIT) has emerged as a promising approach for large-area, distributed tactile sensing with minimal electrode requirements which can lend itself to addressing complex contact problems in robotics. However, existing EIT-based tactile reconstruction methods often suffer from high computational costs or depend on extensive annotated simulation datasets, hindering its viability in real-world settings. To address this shortcoming, here we propose a Pre-trained Transformer for EIT-based Tactile Reconstruction (PTET), a learning-based framework that bridges the simulation-to-reality gap by leveraging self-supervised pretraining on simulation data and fine-tuning with limited real-world data. In simulations, PTET requires 99.44 percent fewer annotated samples than equivalent state-of-the-art approaches (2,500 vs. 450,000 samples) while achieving reconstruction performance improvements of up to 43.57 percent under identical data conditions. Fine-tuning with real-world data further enables PTET to overcome discrepancies between simulated and experimental datasets, achieving superior reconstruction and detail recovery in practical scenarios. The improved reconstruction accuracy, data efficiency, and robustness in real-world tasks establish it as a scalable and practical solution for tactile sensing systems in robotics, especially for object handling and adaptive grasping under varying pressure conditions.