标题： MLLM-Fabric：多模态大语言模型驱动的布料分类与选择机器人框架 显示英文标题

标题： MLLM-Fabric: Multimodal Large Language Model-Driven Robotic Framework for Fabric Sorting and Selection

摘要： 选择合适的面料对于满足纺织制造、服装生产和智能零售中机器人应用的功能和质量要求至关重要。我们提出了MLLM-Fabric，一个由多模态大语言模型（MLLMs）驱动的机器人框架，用于面料分类和选择。该系统包括机械臂、摄像头、视觉触觉传感器和压力传感器。它采用监督微调和多模态解释引导的知识蒸馏来准确分类和排序面料属性。为了促进进一步的研究，我们发布了包含220个独特面料样本的数据集，包括RGB图像以及同步的视觉触觉和压力数据。实验结果表明，我们的Fabric-Llama-90B模型在属性排序准确性和选择可靠性方面始终优于预训练的视觉-语言基线。 显示英文摘要

摘要： 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.