MLLM-Fabric: Multimodal Large Language Model-Driven Robotic Framework for Fabric Sorting and Selection
Liman Wang, Hanyang Zhong, Tianyuan Wang, Shan Luo, Jihong Zhu
Abstract
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.
BibTeX
@inproceedings{ral2025_mllmfabricmultim,
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},
booktitle = {RA-L 2025},
year = {2025}
}