Enhancing Tactile Sensing in Robotics Using Null-Space Diffusion Model with EIT-based Sensors

Robotic tactile sensors based on Electrical Impedance Tomography (EIT) have gained great attention in robotic sensing applications due to their features such as no internal wiring, "all-in-one" structure, and continuous sensing capabilities. However, the effectiveness of EIT-based tactile sensors is hampered by limited spatial resolution and artifacts in the reconstructed images. To address these challenges, various iterative optimization methods based on spatial regularizations and model-based methods have been proposed. In this study, a new EIT reconstruction method using null-space decomposition based on a diffusion model (NSDM) is proposed. Specifically, NSDM consists of a forward diffusion process that first gradually adds Gaussian noise to a clean conductivity image, followed by a backward process that learns to predict the noise that should be removed during each sampling step, utilizing a prior to ensure that the denoising process does not deviate from the correct direction. NSDM requires no training, no optimization, and only requires a pre-prepared diffusion model. Experimental results (both simulation and actual tests) demonstrate that the proposed method outperforms existing generation methods and provides higher quality reconstruction, providing a new solution for robotic tactile sensing in real scenarios.

Authors

• Qilin Zhang
• Haofeng Chen
• Xuanxuan Yang
• Gang Ma
• Xiaojie Wang

Keywords

• Wiring
• Training
• Electrical impedance tomography
• Tactile sensors
• Optimization methods
• Reconstruction algorithms
• Diffusion models
• Sensor systems
• Spatial resolution
• Image reconstruction
• Diffusion Model
• Tactile Sensor
• Impedance
• Gaussian Noise
• Reconstruction Method
• Clear Image
• Iterative Optimization
• Model-based Methods
• Forward Process
• Backward Process
• High-quality Reconstruction
• Neural Network
• Conductive
• Convolutional Neural Network
• Deep Neural Network
• Finite Element
• Generation Process
• Finite Element Method
• Generative Adversarial Networks
• Multiple Inclusions
• Optimization-based Methods
• Inversion Process
• Structural Similarity Index Measure
• Inverse Problem
• Peak Signal-to-noise Ratio
• Data Acquisition Board
• Reconstruction Results
• Current Time Step
• Discrete Fourier Transform