Language Models are Spacecraft Operators

• URL: http://arxiv.org/abs/2404.00413v1
• Date: Sat, 30 Mar 2024 16:43:59 GMT
• Title: Language Models are Spacecraft Operators
• Authors: Victor Rodriguez-Fernandez, Alejandro Carrasco, Jason Cheng, Eli Scharf, Peng Mun Siew, Richard Linares,
• Abstract summary: Large Language Models (LLMs) are autonomous agents that take actions based on the content of the user text prompts. We have developed a pure LLM-based solution for the Kerbal Space Program Differential Games (KSPDG) challenge.
• Score: 36.943670587532026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• LLM-PySC2: Starcraft II learning environment for Large Language Models [16.918044347226104]
  This paper introduces a new environment that serves to develop Large Language Models (LLMs) based decision-making methodologies. This environment is the first to offer the complete StarCraft II action space, multi-modal observation interfaces, and a structured game knowledge database.
  arXiv  Detail & Related papers  (2024-11-08T06:04:22Z)
• Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program [42.87968485876435]
  This study explores the use of fine-tuned Large Language Models (LLMs) for autonomous spacecraft control. We demonstrate how these models can effectively control spacecraft using language-based inputs and outputs.
  arXiv  Detail & Related papers  (2024-08-16T11:43:31Z)
• Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space Exploration [0.0]
  This work explores the application of Large Language Models (LLMs) as the high-level control system of a spacecraft. A series of deep space mission scenarios simulated within the popular game engine Kerbal Space Program are used as case studies to evaluate the implementation against the requirements.
  arXiv  Detail & Related papers  (2024-04-13T03:33:17Z)
• An Embarrassingly Simple Approach for LLM with Strong ASR Capacity [56.30595787061546]
  We focus on solving one of the most important tasks in the field of speech processing, with speech foundation encoders and large language models (LLM) Recent works have complex designs such as compressing the output temporally for the speech encoder, tackling modal alignment for the projector, and utilizing parameter-efficient fine-tuning for the LLM. We found that delicate designs are not necessary, while an embarrassingly simple composition of off-the-shelf speech encoder, LLM, and the only trainable linear projector is competent for the ASR task.
  arXiv  Detail & Related papers  (2024-02-13T23:25:04Z)
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges [44.92286030322281]
  Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation.
  arXiv  Detail & Related papers  (2024-01-21T23:36:14Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [87.1164964709168]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.