ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915548093218816 |
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| author | Mousist, Alejandro D. |
| author_facet | Mousist, Alejandro D. |
| contents | This paper presents ASTREA, the first agentic system executed on flight-heritage hardware (TRL 9) for autonomous spacecraft operations, with on-orbit operation aboard the International Space Station (ISS). Using thermal control as a representative use case, we integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms. Ground experiments show that LLM-guided supervision improves thermal stability and reduces violations, confirming the feasibility of combining semantic reasoning with adaptive control under hardware constraints. On-orbit validation aboard the ISS initially faced challenges due to inference latency misaligned with the rapid thermal cycles of Low Earth Orbit (LEO) satellites. Synchronization with the orbit length successfully surpassed the baseline with reduced violations, extended episode durations, and improved CPU utilization. These findings demonstrate the potential for scalable agentic supervision architectures in future autonomous spacecraft. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_13380 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy Mousist, Alejandro D. Robotics Artificial Intelligence Machine Learning Multiagent Systems Systems and Control This paper presents ASTREA, the first agentic system executed on flight-heritage hardware (TRL 9) for autonomous spacecraft operations, with on-orbit operation aboard the International Space Station (ISS). Using thermal control as a representative use case, we integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms. Ground experiments show that LLM-guided supervision improves thermal stability and reduces violations, confirming the feasibility of combining semantic reasoning with adaptive control under hardware constraints. On-orbit validation aboard the ISS initially faced challenges due to inference latency misaligned with the rapid thermal cycles of Low Earth Orbit (LEO) satellites. Synchronization with the orbit length successfully surpassed the baseline with reduced violations, extended episode durations, and improved CPU utilization. These findings demonstrate the potential for scalable agentic supervision architectures in future autonomous spacecraft. |
| title | ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy |
| topic | Robotics Artificial Intelligence Machine Learning Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2509.13380 |