AI-based worker guidance in assembly and disassembly operations using multimodal ego/exo-centric data capture and structured task knowledge

Abstract: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: this https URL

5 pages. Published in CIRP Annals - Manufacturing Technology

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2608.22617 [cs.CV]

(or arXiv:2608.22617v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.22617 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1016/j.cirp.2026.04.066 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.