本文介绍了一种基于多模态大语言模型（MLLMs）的机器人框架MLLM-Fabric，用于纺织面料的分类与选择。该系统结合机器人臂、相机、视觉触觉传感器和压力传感器，通过监督微调和多模态解释引导的知识蒸馏技术，准确分类和排序面料特性。实验结果表明，该模型在面料特性排序准确性和选择可靠性方面优于预训练的视觉语言基线。

arXiv:2507.04351v1 Announce Type: cross Abstract: Choosing the right fabric is crucial to meet functional and quality requirements in robotic applications for textile manufacturing, apparel production, and smart retail. We present MLLM-Fabric, a robotic framework powered by multimodal large language models (MLLMs) for fabric sorting and selection. The system includes a robotic arm, a camera, a visuotactile sensor, and a pressure sensor. It employs supervised fine-tuning and multimodal explanation-guided knowledge distillation to accurately classify and rank fabric properties. To facilitate further research, we release a dataset of 220 unique fabric samples, including RGB images and synchronized visuotactile and pressure data. Experimental results show that our Fabric-Llama-90B model consistently outperforms pretrained vision-language baselines in both property ranking accuracy and selection reliability.