为太空任务设计智能助手，结合知识图谱与增强现实，提升操作可靠性。
AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues
- 融合知识图谱与生成式模型，实现精准流程支持
- 支持离线运行，响应风格可灵活调整
- 适合航天员在空间站等复杂环境中使用
本文介绍了面向国际空间站（ISS）、月球门户站（Lunar Gateway）及更远深空任务的智能个人助手CORE（Checklist Organizer for Research and Exploration）的能力与潜力。强调可靠且灵活的离线助手对太空任务的重要性，并指出当前航天用智能助手在交互方式和可用性方面存在不足。为此，我们提出一种结合知识图谱（KG）、检索增强生成（RAG）的生成预训练变换器（GPT）以及增强现实（AR）提示元素的系统。该系统通过直观的音视频交互展示检查清单信息，确保流程理解清晰、运行可靠、离线可用，并支持响应风格定制与程序动态更新，显著提升宇航员执行复杂任务的效率与安全性。
原文摘要 · Abstract (English)
This paper describes the capabilities and potential of the intelligent personal assistant (IPA) CORE (Checklist Organizer for Research and Exploration), designed to support astronauts during procedures onboard the International Space Station (ISS), the Lunar Gateway station, and beyond. We reflect on the importance of a reliable and flexible assistant capable of offline operation and highlight the usefulness of audiovisual interaction using augmented reality elements to intuitively display checklist information. We argue that current approaches to the design of IPAs in space operations fall short of meeting these criteria. Therefore, we propose CORE as an assistant that combines Knowledge Graphs (KGs), Retrieval-Augmented Generation (RAG) for a Generative Pre-Trained Transformer (GPT), and Augmented Reality (AR) elements to ensure an intuitive understanding of procedure steps, reliability, offline availability, and flexibility in terms of response style and procedure updates.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。