5 papers
Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems
Amit Jain, Richard Linares
Spacecraft guidance, navigation, and control functions are increasingly realized as learned policies distilled from expert solvers. Developing such a policy is itself a research pr…
Autonomous Reasoning for Spacecraft Control: A Large Language Model Framework with Group Relative Policy Optimization
Amit Jain, Richard Linares
This paper presents a learning-based guidance-and-control approach that couples a reasoning-enabled Large Language Model (LLM) with Group Relative Policy Optimization (GRPO). A two…
Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Control
Amit Jain, Richard Linares
Neural network controllers increasingly demand millions of parameters, and language model approaches push into the billions. For embedded aerospace systems with strict power and la…
Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning
Amit Jain, Victor Rodriguez-Fernandez, Richard Linares
Autonomous spacecraft control for mission phases such as launch, ascent, stage separation, and orbit insertion remains a critical challenge due to the need for adaptive policies th…
Visual Language Models as Operator Agents in the Space Domain
Alejandro Carrasco, Marco Nedungadi, Enrico M. Zucchelli +3
This paper explores the application of Vision-Language Models (VLMs) as operator agents in the space domain, focusing on both software and hardware operational paradigms. Building…