Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program
This addresses the problem of autonomous space operations for researchers and engineers, but it is incremental as it applies existing fine-tuning methods to a new domain.
This study tackled autonomous spacecraft control by fine-tuning LLMs like GPT-3.5 and LLaMA to generate control actions from language-based inputs in the Kerbal Space Program environment, demonstrating their effectiveness as an alternative to traditional Reinforcement Learning approaches.
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 prompt. This study explores the use of fine-tuned Large Language Models (LLMs) for autonomous spacecraft control, using the Kerbal Space Program Differential Games suite (KSPDG) as a testing environment. Traditional Reinforcement Learning (RL) approaches face limitations in this domain due to insufficient simulation capabilities and data. By leveraging LLMs, specifically fine-tuning models like GPT-3.5 and LLaMA, we demonstrate how these models can effectively control spacecraft using language-based inputs and outputs. Our approach integrates real-time mission telemetry into textual prompts processed by the LLM, which then generate control actions via an agent. The results open a discussion about the potential of LLMs for space operations beyond their nominal use for text-related tasks. Future work aims to expand this methodology to other space control tasks and evaluate the performance of different LLM families. The code is available at this URL: \texttt{https://github.com/ARCLab-MIT/kspdg}.