Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models

2026-04-18

AI Summary

This work addresses the challenge that existing spacecraft trajectory optimization relies heavily on manual modeling, hindering safe and autonomous decision-making directly from high-level mission intent. To bridge this gap, the authors propose a novel autonomy framework that leverages behavioral sequences and waypoint constraints as intermediate abstractions to decouple high-level semantic reasoning from low-level safety-critical trajectory optimization. The approach employs a foundation model to generate intent-aligned behavioral plans, which are then refined into dynamically feasible trajectories through a waypoint generation module coupled with an optimization-based safety solver. Evaluated in close-proximity operations, the method achieves over 90% convergence rate using sequential convex programming (SCP) and yields a 1.5× improvement in the proportion of generated trajectories satisfying high-level intent specifications compared to heuristic baselines.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• spacecraft autonomy
• intent-aligned guidance
• trajectory optimization
• mission intent
• safety-critical autonomy

Innovation

• intent-aligned autonomy
• behavior abstraction
• waypoint constraints
• trajectory optimization
• foundation models