A Large Language Model-Based Autonomous Agent for Adaptive Space Exploration Missions

Keskeiset käsitteet

This work explores the use of Large Language Models (LLMs) as the high-level control system for an autonomous spacecraft to enable greater levels of onboard decision-making and mission adaptation.

Tiivistelmä

This work presents the design and development of an agentic spacecraft control system called LLMSat that leverages a Large Language Model (LLM) as the reasoning engine. The key insights are:

LLMSat is designed to enable higher levels of spacecraft autonomy by empowering the onboard LLM to plan and execute goal-oriented mission operations with minimal reliance on ground control.

The architecture features an LLM-based agent that can interpret natural language directives, reason about the spacecraft's state and environment, and generate plans and actions to achieve mission objectives.

The system was evaluated through a series of simulated deep space exploration scenarios in the Kerbal Space Program (KSP) environment.

The results show that while present-day LLMs exhibit promising reasoning and planning capabilities, their performance degrades as the complexity of the mission increases. This can be mitigated through careful prompt engineering and by strategically scoping the agent's level of authority over the spacecraft.

The work identifies key design considerations and verification strategies for future agentic spacecraft systems that leverage LLMs or similar neuro-symbolic agents.

Tilastot

"Reducing reliance on human-based mission control becomes increasingly critical if we are to increase our rate of solar-system-wide exploration."
"The cost to operate OSIRIS-REx over its 9-year primary mission lifespan was USD$283 million, accounting for over 50% of its development costs."
"If greater levels of onboard autonomy can reduce the operating costs of active missions by just 10%, the direct savings from this reduction alone would be sufficient to fund entirely new missions."

Lainaukset

"Never forget I am not this silver body, Mahrai. I am not an animal brain, I am not even some attempt to produce an AI through software running on a computer. I am a Culture Mind. We are close to gods, and on the far side."

Tärkeimmät oivallukset

LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space Exploration

by David Marant... klo arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01392.pdf

Syvällisempiä Kysymyksiä

How can the safety and reliability of an LLM-based agentic spacecraft be verified before launch?

Before launching an LLM-based agentic spacecraft, several verification steps can be taken to ensure its safety and reliability. These steps include:

Simulation Testing: Conduct extensive simulation testing using realistic scenarios to evaluate the spacecraft's performance in various conditions. This allows for the identification of potential issues and the refinement of the LLM's decision-making processes.

Validation Against Ground Truth: Compare the LLM's decisions and actions against ground truth data or expert knowledge to verify the accuracy of its reasoning and planning capabilities.

Failure Mode Analysis: Perform failure mode analysis to identify potential failure points in the LLM-based system and develop mitigation strategies to address them.

Redundancy and Error Handling: Implement redundancy in critical systems and develop robust error-handling mechanisms to ensure the spacecraft can recover from unexpected situations.

Ethical and Legal Compliance: Ensure that the LLM-based system complies with ethical guidelines and legal regulations related to autonomous systems in space exploration.

Peer Review and Expert Evaluation: Seek input from domain experts and conduct peer reviews to validate the system's design and decision-making processes.

By following these verification steps, the safety and reliability of an LLM-based agentic spacecraft can be thoroughly assessed before launch.

What are the potential risks and ethical considerations of granting high levels of autonomy to an LLM-based spacecraft controller?

Granting high levels of autonomy to an LLM-based spacecraft controller comes with several potential risks and ethical considerations, including:

Safety Concerns: There is a risk of the spacecraft making incorrect decisions or actions due to limitations in the LLM's training data or understanding of complex scenarios, leading to potential accidents or mission failures.

Accountability: Determining accountability in case of errors or accidents involving an autonomous spacecraft controlled by an LLM raises ethical questions about responsibility and liability.

Data Bias: LLMs are trained on large datasets, which may contain biases that could influence the spacecraft's decision-making process, potentially leading to unfair or discriminatory outcomes.

Privacy and Security: Autonomous systems may raise concerns about data privacy and security, especially if sensitive information is processed or transmitted by the spacecraft without proper safeguards.

Autonomy vs. Human Control: Balancing the autonomy of the spacecraft with human oversight and intervention is crucial to ensure that critical decisions can be overridden in case of emergencies or unforeseen circumstances.

Long-Term Implications: The long-term implications of deploying highly autonomous spacecraft controlled by LLMs, including societal impacts, economic considerations, and the potential for unintended consequences, must be carefully considered.

Addressing these risks and ethical considerations requires a comprehensive ethical framework, robust safety protocols, and ongoing monitoring and evaluation of the spacecraft's autonomous capabilities.

How could the capabilities of LLMs be extended to enable more robust and scalable reasoning and planning for complex space missions?

To enhance the capabilities of LLMs for more robust and scalable reasoning and planning in complex space missions, several strategies can be implemented:

Multi-Modal Integration: Incorporate multi-modal capabilities into LLMs to process and understand different types of data, such as images, videos, and sensor inputs, enabling a more comprehensive understanding of the spacecraft's environment.

Incremental Learning: Implement incremental learning techniques to allow the LLM to continuously update its knowledge and adapt to new information, improving its decision-making abilities over time.

Hybrid Models: Combine LLMs with other AI techniques, such as reinforcement learning or symbolic reasoning, to create hybrid models that leverage the strengths of each approach for more effective planning and execution.

Domain-Specific Training: Train LLMs on domain-specific data related to space missions to enhance their understanding of mission objectives, constraints, and challenges, enabling more contextually relevant decision-making.

Real-Time Adaptation: Develop mechanisms for real-time adaptation and dynamic decision-making based on changing mission requirements, environmental conditions, and unexpected events during the mission.

Collaborative Autonomy: Implement collaborative autonomy frameworks that allow LLM-based spacecraft controllers to interact with other autonomous systems, human operators, and external resources to optimize mission performance and adapt to complex scenarios.

By incorporating these strategies, LLMs can be extended to handle the complexities of space missions more effectively, enabling them to reason, plan, and execute tasks autonomously with a higher degree of reliability and scalability.