Towards Robust Spacecraft Trajectory Optimization via Transformers

Future multi-spacecraft missions require robust au-tonomous trajectory optimization capabilities to ensure safe and efficient rendezvous operations. This capability hinges on solving non-convex optimal control problems in real-time, although traditional iterative methods such as sequential convex programming impose significant computational challenges. To mitigate this burden, the Autonomous Rendezvous Transformer (ART) [1] introduced a generative model trained to provide near-optimal initial guesses. This approach provides conver-gence to better local optima (e.g., fuel optimality), improves feasibility rates, and results in faster convergence speed of opti-mization algorithms through warm-starting. This work extends the capabilities of ART to address robust chance-constrained optimal control problems. Specifically, ART is applied to challenging rendezvous scenarios in Low Earth Orbit (LEO), ensuring fault-tolerant behavior under uncertainty. Through extensive experimentation, the proposed warm-starting strat-egy is shown to consistently produce high-quality reference trajectories, achieving up to 30 % cost improvement and 50 % reduction in infeasible cases compared to conventional methods, demonstrating robust performance across multiple state repre-sentations. Additionally, a post hoc evaluation framework is proposed to assess the quality of generated trajectories and miti-gate runtime failures, marking an initial step toward the reliable deployment of AI-driven solutions in safety-critical autonomous systems such as spacecraft. Project website and Code https://robust-art.github