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

2025-06-03

AI Summary

To address the high computational cost and heavy reliance on large-scale annotated simulation data in electrical impedance tomography (EIT) reconstruction for high-resolution, large-area robotic tactile sensing, this paper proposes a lightweight and efficient reconstruction framework integrating soft EIT hardware with a pre-trained Transformer model. Methodologically, it introduces a novel self-supervised simulation pre-training followed by fine-tuning with minimal real-world data, effectively mitigating the simulation-to-reality domain gap. Remarkably, only 2,500 simulated samples—99.44% fewer than state-of-the-art approaches—are required to achieve high-fidelity contact force distribution reconstruction, yielding a 43.57% improvement in reconstruction accuracy. Experimental validation confirms significantly enhanced recovery of pressure fine-grained details, enabling robust and adaptive robotic grasping.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Achieving high-resolution large-area tactile sensing in robotics
• Reducing computational costs in EIT-based tactile reconstruction
• Bridging simulation-to-reality gap in tactile sensing systems

Innovation

• Soft EIT for large-area tactile sensing
• Pre-trained Transformer reduces data needs
• Self-supervised pretraining bridges simulation-reality gap