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Language Models are Spacecraft Operators

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arxiv 2404.00413 v1 pith:WFV2C5CF submitted 2024-03-30 physics.space-ph cs.AIcs.LG

Victor Rodriguez-Fernandez , Alejandro Carrasco , Jason Cheng , Eli Scharf , Peng Mun Siew , Richard Linares This is my paper · ORCID
classification physics.space-phcs.AIcs.LG
keywords spaceagentsautonomouscompetitioncreatekspdglanguagellm-based
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts. We intend to apply these concepts to the field of Guidance, Navigation, and Control in space, enabling LLMs to have a significant role in the decision-making process for autonomous satellite operations. As a first step towards this goal, we have developed a pure LLM-based solution for the Kerbal Space Program Differential Games (KSPDG) challenge, a public software design competition where participants create autonomous agents for maneuvering satellites involved in non-cooperative space operations, running on the KSP game engine. Our approach leverages prompt engineering, few-shot prompting, and fine-tuning techniques to create an effective LLM-based agent that ranked 2nd in the competition. To the best of our knowledge, this work pioneers the integration of LLM agents into space research. Code is available at https://github.com/ARCLab-MIT/kspdg.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches

    cs.NI 2025-07 conditional novelty 5.0 of 10

    A survey that maps five generative model families, from variational autoencoders to large language models, onto agentic AI roles in satellite-augmented low-altitude economy and terrestrial networks.

  2. Large Language Models as Autonomous Spacecraft Operators in Kerbal Space Program

    cs.AI 2025-05 conditional novelty 5.0 of 10

    LLM agents using prompt engineering and fine-tuning ranked second in the Kerbal Space Program Differential Games pursuit-evasion challenge.

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