Choosing appropriate fabrics is critical for meeting functional and quality demands in robotic textile manufacturing, apparel production, and smart retail. We propose MLLM-Fabric, a robotic framework leveraging multimodal large language models (MLLMs) for fabric sorting and selection. Built on a multimodal robotic platform, the system is trained through supervised fine-tuning and explanation-guided distillation to rank fabric properties. We also release a dataset of 220 diverse fabrics, each with RGB images and synchronized visuotactile and pressure data. Experiments show that our Fabric-Llama-90B consistently outperforms pretrained vision-language baselines in both attribute ranking and selection reliability. Code and dataset are publicly available at https://github.com/limanwang/MLLM-Fabric.
@article{arxiv.2507.04351,
title = {MLLM-Fabric: Multimodal Large Language Model-Driven Robotic Framework for Fabric Sorting and Selection},
author = {Liman Wang and Hanyang Zhong and Tianyuan Wang and Shan Luo and Jihong Zhu},
journal= {arXiv preprint arXiv:2507.04351},
year = {2025}
}
Comments
Accepted to IEEE Robotics and Automation Letters (RAL)