Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems
AI that can teach spacecraft to fly themselves—and prove the results are real
Researchers built an AI agent that automatically designs control policies for spacecraft by proposing and testing tweaks to training code, then checking whether improvements are genuine or just statistical noise. On two docking and rendezvous problems, the AI-designed policies outperformed random parameter searches so decisively that on one task, undirected search produced no working solution at all while the AI approach succeeded every time.
Spacecraft currently rely on hand-coded control systems or policies developed through labor-intensive manual research. This framework could compress that development cycle while building in built-in verification that results are trustworthy—crucial for safety-critical aerospace applications where false confidence in a control system could end in collision or mission failure.