Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models

Yuji Takubo, Simone D'Amico

Keywords: Spacecraft autonomy, Reasoning models, Trajectory Optimization

TL;DR: A hierarchical reasoning-to-optimization pipeline that translates high-level mission intent into safe, dynamically feasible spacecraft trajectories.

Abstract:

Future spacecraft operations require autonomy that can interpret high-level mission intent while preserving safety.
However, existing trajectory optimization still relies heavily on expert-crafted formulations and does not support intent-conditioned decision-making.
This paper proposes an intent-aligned spacecraft guidance framework that links high-level reasoning and safe trajectory optimization through explicit intermediate abstractions, based on behavior sequences and waypoint constraints.
A foundation model first predicts an intent-aligned behavior plan, a waypoint generation model then converts it into waypoint constraints, and the safe trajectory is computed via optimization.
This decomposition enables scalable supervision without sacrificing safety.
Numerical experiments in close-proximity operation scenarios demonstrate that the proposed pipeline achieves over 90\% SCP convergence and yields a 1.5$\times$ higher rate of generating trajectories that satisfy the top intent-prioritized performance criteria than heuristic decision-making.
These results support the use of intermediate behavior abstraction as a practical interface between foundation-model reasoning and safety-critical onboard spacecraft autonomy.

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Submission Number: 19