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.

翻译：本文介绍了ASTREA，这是首个在具有飞行传承的硬件（TRL 9）上执行、用于自主航天器操作并在国际空间站（ISS）上实现在轨运行的智能体系统。以热控制作为代表性用例，我们将一个资源受限的大型语言模型（LLM）智能体与一个强化学习控制器集成在一个为空间适用平台定制的异步架构中。地面实验表明，LLM引导的监督提高了热稳定性并减少了违规情况，证实了在硬件约束下将语义推理与自适应控制相结合的可行性。在国际空间站上的在轨验证最初面临挑战，原因是推理延迟与低地球轨道（LEO）卫星的快速热循环周期不匹配。通过与轨道长度同步，系统成功超越了基线性能，表现为违规减少、任务周期延长以及CPU利用率提高。这些发现证明了可扩展的智能体监督架构在未来自主航天器中的潜力。