基于多模态自我/外部中心数据采集与结构化任务知识的装配与拆卸作业中基于人工智能的工人指导
AI-based worker guidance in assembly and disassembly operations using multimodal ego/exo-centric data capture and structured task knowledge
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中文总结 AI 辅助
该研究提出一种从专家演示中提取结构化任务知识的以数据为中心的方法，结合多模态数据推导任务表示，实现装配拆卸作业的工人指导，经案例验证其优于静态图像方法，可用于维修、培训等场景。
中文摘要 AI 辅助
装配与拆卸过程依赖难以记录、复用和传递的专家知识。本文提出一种以数据为中心的方法，用于从专家演示中利用自我中心（egocentric）与外部中心（exocentric）记录提取结构化任务知识。联合编码视频与旁白的时间及多模态信息，以推导支持流程记录与上下文感知工人指导的结构化任务表示。该方法在真实拆卸案例研究中评估，结果表明基于视频的表示捕获了超出静态图像方法的流程结构与执行上下文，凸显自我中心视频理解在维修、培训及循环制造应用中的潜力。项目网站：this https URL
英文摘要
Assembly and disassembly processes rely on expert knowledge that is difficult to document, reuse, and transfer. This paper presents a data-centric approach for extracting structured task knowledge from expert demonstrations using egocentric and exocentric recordings. Temporal and multimodal information from video and narration is jointly encoded to derive structured task representations that enable procedural documentation and context-aware worker guidance. The approach is evaluated on a real-world disassembly case study, demonstrating that video-based representations capture procedural structure and execution context beyond static image-based methods. The results highlight the potential of egocentric video understanding for repair, training, and circular manufacturing applications. Project website: https://indego-assistant.github.io/
