MLLM-Fabric: A Robot Fabric Sorting Framework Driven by Multimodal Large Language Models

MLLM-Fabric deeply integrates multimodal large language models (MLLMs) with robotics technology. Addressing the pain points in the textile industry such as low efficiency, high error rate of manual sorting, and poor adaptability of traditional automation solutions, it builds an end-to-end intelligent fabric sorting framework. This provides an innovative solution for textile industry automation and also offers reference ideas for the intelligent upgrading of manufacturing.

Fabric sorting is a key link in the textile industry chain, but traditional methods have four major pain points: difficulty in unified modeling due to material diversity, complex texture recognition challenges, accuracy affected by light sensitivity, and increased operational complexity from flexible deformation. Multimodal large language models, through image-text pre-training, possess visual content understanding and semantic description capabilities, providing new ideas to solve these problems.

The core of the MLLM-Fabric framework is a closed loop of "Perception-Understanding-Execution", consisting of four modules:

1. Multimodal Perception Layer: Extracts high-level semantic features from images and understands fabric attributes (e.g., "lightweight cotton-linen blended fabric");
2. Semantic Understanding Engine: Receives visual features, generates classification decisions combined with task instructions, and has strong generalization ability;
3. Robot Control Interface: Maps semantic decisions to operations such as grabbing/moving, adapting to different robotic arms;
4. Feedback Learning Mechanism: Updates the model based on task results to achieve self-optimization.

MLLM-Fabric can be applied in multiple scenarios:

• Intelligent Garment Manufacturing: Automatically identifies the attributes of cut pieces and guides robots to perform precise pairing;
• Textile Quality Inspection: Detects defects/color differences and sorts out unqualified products;
• Second-hand Garment Recycling: Identifies material/brand/condition to support recycling;
• Customized Home Textile Production: Matches customer sample fabrics with inventory fabrics to shorten response time.

Current Challenges:

1. Real-Time Performance: Need to meet industrial real-time requirements through model distillation and edge computing;
2. Data Privacy: Need to use federated learning and differential privacy to protect enterprise data;
3. Cross-Domain Generalization: Need to improve the model's ability to adapt to new scenarios. Future Outlook: With the advancement of MLLM technology and cost reduction, it is expected to be applied to more manufacturing scenarios such as metal parts, food, and electronic products.

MLLM-Fabric proves that large language models can create value in the real economy. It is an important direction for the deep integration of AI and traditional manufacturing, providing a valuable reference case for developers and enterprises implementing AI applications.