mlsanityData to 7 Oct 2026

AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues

arXiv 2409.14206 ↗First posted 21 Sep 2024cs.AI cs.HCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 21 Sep 2024.

Builds on

17 of 51 references.

References · 17

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  5. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
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  10. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  11. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
  12. 2023Atom: Low-bit Quantization for Efficient and Accurate LLM Servingcited by 255
5 more
  1. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 217
  2. 2022Multi-Modal Knowledge Graph Construction and Application: A Surveycited by 74
  3. 2024Style Vectors for Steering Generative Large Language Modelcited by 56
  4. 2023Why Does ChatGPT Fall Short in Providing Truthful Answers?cited by 54
  5. 2023Augmented Reality for Maintenance Tasks with ChatGPT for Automated Text-to-Actioncited by 2

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Citing papers per year: 2024: 1.

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  1. 2026A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations
  2. 2026JARVIS: A Just-in-Time Augmented Reality VLM-Powered Instruction System for Cross-Reality Task Guidance ↗
  3. 2026A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
  4. 2025From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry
  5. 2025An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance
  6. 2026SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing
  7. 2026COSMIC: Emotionally Intelligent Agents to Support Mental and Emotional Well-being in Extreme Isolation: Lessons from Analog Astronaut Training Missions
  8. 2024HuBar: A Visual Analytics Tool to Explore Human Behaviour based on fNIRS in AR guidance systems