用深度学习让机器人读懂织物图案，自动生成精准编织指令。
Knitting Robots: A Deep Learning Approach for Reverse-Engineering Fabric Patterns
- 两阶段视觉识别+标签推断，实现织物图案逆向生成。
- 支持单纱与多纱复杂结构，适应不同材料特性。
- 解决标签不平衡与细粒度控制难题，适合智能纺织产线。
针织作为纺织制造的核心工艺，自动化难度高，尤其在将织物设计转化为精确可执行的机器指令方面面临挑战。本文提出一种基于深度学习的逆向针织新方法，通过两阶段架构，使机器人能够先识别正面标签，再推断完整标签，实现高精度、可扩展的图案生成。系统支持单纱（sj）和多纱（mj）等多种纱线结构，有效应对材料复杂性差异。针对机器人纺织操作中的标签不平衡、稀有针法缺失及细粒度控制等关键问题，采用专用深度学习模型进行优化。本研究为全自动机器人针织系统奠定基础，实现感知、规划与执行一体化，推动纺织制造向智能化、柔性化发展。
原文摘要 · Abstract (English)
Knitting, a cornerstone of textile manufacturing, is uniquely challenging to automate, particularly in terms of converting fabric designs into precise, machine-readable instructions. This research bridges the gap between textile production and robotic automation by proposing a novel deep learning-based pipeline for reverse knitting to integrate vision-based robotic systems into textile manufacturing. The pipeline employs a two-stage architecture, enabling robots to first identify front labels before inferring complete labels, ensuring accurate, scalable pattern generation. By incorporating diverse yarn structures, including single-yarn (sj) and multi-yarn (mj) patterns, this study demonstrates how our system can adapt to varying material complexities. Critical challenges in robotic textile manipulation, such as label imbalance, underrepresented stitch types, and the need for fine-grained control, are addressed by leveraging specialized deep-learning architectures. This work establishes a foundation for fully automated robotic knitting systems, enabling customizable, flexible production processes that integrate perception, planning, and actuation, thereby advancing textile manufacturing through intelligent robotic automation.
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