# Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models

arXiv: https://arxiv.org/abs/2604.17176 · HTML: https://mlsanity.com/paper/2604.17176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 19 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [Language Conditioned Imitation Learning over Unstructured Data](https://mlsanity.com/paper/2005.07648/) (2020)
- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous](https://mlsanity.com/paper/2310.13831/) (2023)
- [Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications](https://mlsanity.com/paper/2408.05924/) (2024)
- [Successive Convexification of Non-Convex Optimal Control Problems and Its Convergence Properties](https://mlsanity.com/paper/1608.05133/) (2016)
- [Towards Robust Spacecraft Trajectory Optimization via Transformers](https://mlsanity.com/paper/2410.05585/) (2024)
- [GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming](https://mlsanity.com/paper/1903.00155/) (2019)
- [Successive Convexification with Feasibility Guarantee via Augmented Lagrangian for Non-Convex Optimal Control Problems](https://mlsanity.com/paper/2304.14564/) (2023)
- [Guided Policy Search using Sequential Convex Programming for Initialization of Trajectory Optimization Algorithms](https://mlsanity.com/paper/2110.06975/) (2021)
- [Passively-Safe and Robust Multi-Agent Optimal Control with Application to Distributed Space Systems](https://mlsanity.com/paper/2209.02096/) (2022)
- [Large Language Models as Autonomous Spacecraft Operators in Kerbal Space Program](https://mlsanity.com/paper/2505.19896/) (2025)
- [Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers](https://mlsanity.com/paper/2510.03544/) (2025)
- [Autonomous Reasoning for Spacecraft Control: A Large Language Model Framework with Group Relative Policy Optimization](https://mlsanity.com/paper/2601.04334/) (2026)

## Built on by (most cited first, 1 of 1)

- [OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous](https://mlsanity.com/paper/2610.01093/) (2026)

## Abstract (arXiv)

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× 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.