VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios

• URL: http://arxiv.org/abs/2607.14728v1
• Date: Thu, 16 Jul 2026 08:53:41 GMT
• Title: VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios
• Abstract summary: VQ-Touch is a tactile generation framework that supports both cross-sensor and multi-scenario applications.<n>To efficiently extract complex deformation and texture features from the data, we propose DM-VQGAN.<n>We introduce a discrete diffusion decoder with a unified conditioning interface, supporting multimodal generation tasks such as images and labels.
• Score: 7.9567869558684094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tactile image generation significantly reduces the dependency on expensive and wear-prone sensors by synthesizing high-fidelity tactile data, offering an efficient solution for tactile information acquisition in robotic perception and human-machine interaction systems. However, existing methods depend on large-scale, diverse datasets from specific sensors and lack efficient data utilization and robust generalization capabilities, struggling in vision-limited environments. To address this, we introduce VQ-Touch, a tactile generation framework that supports both cross-sensor and multi-scenario applications. Specifically, to efficiently extract complex deformation and texture features from the data, we propose DM-VQGAN, an effective tactile representation learner. Furthermore, we introduce a discrete diffusion decoder with a unified conditioning interface, supporting multimodal generation tasks such as images and labels, and enhances the model's generalization capability through few-shot mixed training, thus achieving compatibility with current mainstream sensors and their variants. Experiments show that VQ-Touch surpasses state-of-the-art methods in multiple tasks.

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