TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking

2026-08-26

AI Summary

为解决服装制版中因隐性知识导致的技术断层问题，TailorCoPilot通过版本控制状态跟踪技术记录专家的操作过程，并辅助新手学习。

Abstract

Experience-driven manufacturing, such as garment pattern making, faces a severe generational skills gap because its core expertise relies on undocumented tacit knowledge forged through day-to-day practice. To address this challenge, we present TailorCoPilot, an agentic pattern-making system built upon a specially designed version-control backend TailorTrace. TailorTrace models sewing patterns as structured, discrete states and records their transformations during the pattern-making process as explicit operation sequences defined upon the geometry primitives in the sewing pattern (panels, edges, vertices and stitches). Integrated into a conventional pattern-making GUI, TailorTrace enables seamless documentation of senior experts' tacit pattern-making knowledge without breaking their daily workflow. The documented knowledge further offers interactive, pedagogical scaffolding for novices, while providing a robust foundation to power TailorCoPilot and train future generative AI models. In a user study with novices and advanced novices, TailorCoPilot improved task completion rates, reduced time and perceived workload, and yielded higher-quality artifacts compared to skill-appropriate baselines. Ultimately, TailorCoPilot demonstrates a viable pathway to capture practice-based expertise, operationalizing it to support both generative AI advancements and human apprenticeship.

Problem

• experience-driven manufacturing
• garment pattern making
• generational skills gap
• tacit knowledge

Innovation

• Version-Controlled State Tracking
• Tacit Knowledge Documentation
• Generative AI Models
• Interactive Pedagogical Scaffolding