Vision-based Tactile Image Generation via Contact Condition-guided Diffusion Model

TL;DR

This paper introduces a contact-condition guided diffusion model that generates high-fidelity tactile images from RGB and force data, improving simulation accuracy for vision-based tactile sensors in robotics.

Contribution

The proposed diffusion model enhances tactile image generation by incorporating contact conditions, outperforming existing methods in accuracy and texture detail reconstruction.

Findings

60.58% reduction in mean squared error

38.1% reduction in marker displacement error

Effective across various tactile sensors

Abstract

Vision-based tactile sensors, through high-resolution optical measurements, can effectively perceive the geometric shape of objects and the force information during the contact process, thus helping robots acquire higher-dimensional tactile data. Vision-based tactile sensor simulation supports the acquisition and understanding of tactile information without physical sensors by accurately capturing and analyzing contact behavior and physical properties. However, the complexity of contact dynamics and lighting modeling limits the accurate reproduction of real sensor responses in simulations, making it difficult to meet the needs of different sensor setups and affecting the reliability and effectiveness of strategy transfer to practical applications. In this letter, we propose a contact-condition guided diffusion model that maps RGB images of objects and contact force data to high-fidelity, detail-rich vision-based tactile sensor images. Evaluations show that the three-channel tactile images generated by this method achieve a 60.58% reduction in mean squared error and a 38.1% reduction in marker displacement error compared to existing approaches based on lighting model and mechanical model, validating the effectiveness of our approach. The method is successfully applied to various types of tactile vision sensors and can effectively generate corresponding tactile images under complex loads. Additionally, it demonstrates outstanding reconstruction of fine texture features of objects in a Montessori tactile board texture generation task.