Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous

TL;DR

This paper presents SAGES, a framework translating natural language commands into spacecraft trajectories that respect constraints, enabling intuitive, safe, and reliable autonomous rendezvous operations.

Contribution

Introduces SAGES, a novel language-conditioned trajectory generation system that reduces expert input and enhances operational scalability in complex space missions.

Findings

Achieves over 90% semantic-behavioral consistency across diverse modes.

Demonstrates reliable trajectory generation in proximity operations and robotic platform tests.

Enables natural language interaction for spacecraft guidance.

Abstract

Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autonomous trajectory optimization, these methods still rely on extensive expert input (e.g., waypoints, constraints, mission timelines, etc.), which limits operational scalability in complex missions such as rendezvous and proximity operations. This paper introduces SAGES (Semantic Autonomous Guidance Engine for Space), a trajectory-generation framework that translates natural-language commands into spacecraft trajectories that reflect high-level intent while respecting nonconvex constraints. Experiments in two settings (fault-tolerant proximity operations with continuous-time constraint enforcement and a free-flying robotic platform) demonstrate that SAGES reliably produces trajectories aligned with human commands, achieving over 90% semantic-behavioral consistency across diverse behavior modes. Ultimately, this work marks an initial step toward language-conditioned, constraint-aware spacecraft trajectory generation, enabling operators to interactively guide both safety and behavior through intuitive natural-language commands with reduced expert burden. Project Website: https://semantic-guidance4space.github.io/