论文/arXiv 2603.05230·16 天前

数字孪生驱动的自动分拣系统中的纺织品分类与异物识别

提出数字孪生驱动的双臂机器人分拣系统，融合抓取预测、多模态感知与VLM，在真实纺织品分类和异物识别场景验证。

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原文链接：Digital-Twin-driven textile classification and foreign object recognition in automated sorting systems（arXiv 2603.05230 · 发布日期：2026-10-12）

Digital-Twin-driven textile classification and foreign object recognition in automated sorting systems

提出数字孪生驱动的双臂机器人分拣系统，融合抓取预测、多模态感知与VLM，在真实纺织品分类和异物识别场景验证。

核心要点

• 可持续纺织品回收需要在杂乱环境中处理可变形衣物并检测异物。
• 系统结合RGB-D感知、触觉反馈、碰撞感知规划、抓取预测和VLM语义推理。
• 在223个检查场景评测五个模型家族的九个VLM，Qwen总体准确率最高达87.9%。
• 数字孪生与MoveIt结合分割三维点云，支持碰撞感知路径规划并提升操作可靠性。

验证范围与局限：验证范围为一个真实双臂机器人单元和223个检查场景，包含衣物、异物及空场景。

英文摘要 (Abstract)

The increasing demand for sustainable textile recycling requires robust automation solutions capable of handling deformable garments and detecting foreign objects in cluttered environments. This work presents a digital twin–driven robotic sorting system that integrates grasp prediction, multi-modal perception, and semantic reasoning for real-world textile classification. A dual-arm robotic cell equipped with RGB-D sensing, capacitive tactile feedback, and collision-aware motion planning autonomously separates garments from an unsorted basket, transfers them to an inspection zone, and classifies them using state-of-the-art Visual Language Models (VLMs). We benchmark nine VLMs from five model families on a dataset of 223 inspection scenarios comprising shirts, socks, trousers, underwear, foreign objects (including garments outside of the aforementioned classes), and empty scenes. The evaluation assesses per-class accuracy, hallucination behavior, and computational performance under practical hardware constraints. Results show that the Qwen model family achieves the highest overall accuracy (up to 87.9 %), with strong foreign object detection performance, while lighter models such as Gemma3 offer competitive speed–accuracy trade-offs for edge deployment. A digital twin combined with MoveIt enables collision-aware path planning and integrates segmented 3D point clouds of inspected garments into the virtual environment for improved manipulation reliability. The presented system demonstrates the feasibility of combining semantic VLM reasoning with conventional grasp detection and digital twin technology for scalable, autonomous textile sorting in realistic industrial settings.

作者：Serkan Ergun, Tobias Mitterer, Hubert Zangl

来源

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1. arXiv 2603.05230：Digital-Twin-driven textile classification and foreign object recognition in automated sorting systems访问 2026-10-21