Summary of Fine-tuning Llms For Autonomous Spacecraft Control: a Case Study Using Kerbal Space Program, by Alejandro Carrasco et al.
Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program
by Alejandro Carrasco, Victor Rodriguez-Fernandez, Richard Linares
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Instrumentation and Methods for Astrophysics (astro-ph.IM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study explores the application of Large Language Models (LLMs) in autonomous spacecraft control using the Kerbal Space Program Differential Games suite (KSPDG). Fine-tuned LLMs, such as GPT-3.5 and LLaMA, are utilized to effectively control spacecraft through language-based inputs and outputs. The approach integrates real-time mission telemetry into textual prompts processed by the LLM, which then generates control actions via an agent. Traditional Reinforcement Learning (RL) approaches face limitations in this domain due to insufficient simulation capabilities and data. This study demonstrates the potential of LLMs for space operations beyond their nominal use for text-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores using special computers called Large Language Models to control spacecraft. It’s like a game where you give instructions to the computer, and it makes decisions based on what you say. The study uses a special program called Kerbal Space Program to test this idea. They found that these language models can be very good at controlling spacecraft! This could be important for real space missions in the future. |
Keywords
* Artificial intelligence * Gpt * Llama * Reinforcement learning
