An LLM-driven agent with built-in seed-noise audits develops control policies for two aerospace problems that outperform undirected search and pass verification checks.
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Neural surrogates trained on a large homotopy-ray dataset approximate low-thrust fuel consumption and transfer times, obey a scaling law, and generalize via self-similar transformation across semi-major axes, inclinations, and central bodies.
Empirical comparison finds Mamba with PPO superior to LSTM/GRU and SAC variants for meta-RL tuning of ICCBF class-K functions in cooperative and adversarial spacecraft RPO simulations.
citing papers explorer
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Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems
An LLM-driven agent with built-in seed-noise audits develops control policies for two aerospace problems that outperform undirected search and pass verification checks.
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Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability
Neural surrogates trained on a large homotopy-ray dataset approximate low-thrust fuel consumption and transfer times, obey a scaling law, and generalize via self-similar transformation across semi-major axes, inclinations, and central bodies.
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Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations
Empirical comparison finds Mamba with PPO superior to LSTM/GRU and SAC variants for meta-RL tuning of ICCBF class-K functions in cooperative and adversarial spacecraft RPO simulations.