Language Models are Spacecraft Operators (2404.00413v1)

Abstract: Recent trends are emerging in the use of 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.

• The paper establishes that LLMs can autonomously control spacecraft in non-cooperative orbital scenarios using prompt engineering and fine-tuning.
• It details innovative methods like observation augmentation with relative positional and velocity metrics to enhance decision-making accuracy.
• Empirical results show improved response latency and reduced failure rates, underscoring LLMs' viability for real-time space operations.

LLMs Guiding Spacecraft: An In-depth Look at AI-Based Autonomy in KSPDG

Background and Introduction to KSPDG

The Kerbal Space Program Differential Games (KSPDG) challenge has set a novel precedent in the field of autonomous space operation simulations. Based on the Kerbal Space Program (KSP), a space flight simulation game known for its detailed depiction of space exploration mechanics, KSPDG introduces a set of non-cooperative game environments aimed at developing and evaluating autonomous AI decision-makers for orbital operations.

The primary focus of this research lies in the Pursuer-Evader scenario where participants create autonomous agents tasked with maneuvering a satellite to either pursue or evade another. These dynamics present a fertile ground for applying and assessing various AI methodologies, including LLMs, in controlling spacecraft in non-cooperative interactions within an orbital context.

The Intersection of LLMs and Spacecraft Control

Consideration is given to the recent advancements in LLMs and their potential applications beyond text-based tasks. The research demonstrates how a purely LLM-based solution can be utilized for spacecraft maneuvering in the Pursuer-Evader scenario of KSPDG. By feeding the model with current mission states as text prompts and receiving reasoned action responses, the approach leverages the strengths of LLMs to navigate the complexities of space operations.

Enhancing the model's effectiveness involved prompt engineering, few-shot prompting, and fine-tuning with gameplay data, establishing a solid foundation for the model’s intuitive response generation. This strategy circumvents the limitations typically encountered in Reinforcement Learning (RL) frameworks, such as extensive data requirement and definition of precise reward functions, which are especially challenging in the space operation domain.

Application of Prompt Engineering and Observation Augmentation

The research details innovative methods in prompt engineering to improve model performance significantly. By augmenting the observation space with calculated metrics that provide relative positional and velocity information, the model's decision-making accuracy enhanced. These augmentation strategies have shown to directly influence the LLM's operational efficiency, providing a clearer context for the model to generate actionable responses.

Fine-Tuning LLMs with Gameplay Data

The exploration of fine-tuning with human gameplay data introduces an effective methodology to adapt LLMs to specific tasks within the space operation domain. The fine-tuning process not only improved response latency, making the model more suitable for real-time applications, but also significantly reduced the failure rate in task execution. This approach exemplifies the potential of leveraging human intelligence and expertise to enhance autonomous systems in complex environments.

Implications and Future Directions

This research has laid a notable groundwork indicating the viability of employing LLMs for spacecraft control tasks within the scope of KSPDG. The model's performance, validated through competitive results in the challenge, underscores the practicality of LLMs in navigating the complexities of orbital dynamics and maneuvers.

However, the paper also opens dialogues on broader applications and the future of LLMs in spacecraft autonomy. It suggests the exploration of multimodal models to incorporate visual inputs, enhancing decision-making capabilities, and the possibility of generating autonomous agents through LLM-driven code generation processes. Moreover, the scalability of fine-tuning methodologies through diverse and extensive datasets could unlock new horizons for AI in space.

Concluding Remarks

This insightful exploration into the application of LLMs for spacecraft operation tasks within KSPDG provides a promising outlook on the intersection of generative AI and autonomous space exploration. The adaptability, efficiency, and competitive performance of LLMs in controlling spacecraft present a revolutionary step towards leveraging AI in managing the complexities of non-cooperative space operations. As the field of AI continues to evolve, so too will the methodologies for integrating these models into the fabric of space exploration and operation, opening new frontiers for autonomous decision-making beyond the confines of our planet.