The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barriers to trust, such as uncertainty, opaqueness, brittleness, and inflexibility. This paper presents the achievements and lessons learned from the STARS program in integrating reinforcement learning-based multi-satellite control, run time assurance approaches, and flexible human-autonomy teaming interfaces, into a new integrated testing environment for collaborative autonomous satellite systems. The primary results describe analysis of the reinforcement learning multi-satellite control and run time assurance algorithms. These algorithms are integrated into a prototype human-autonomy interface using best practices from human-autonomy trust literature, however detailed analysis of the effectiveness is left to future work. References are provided with additional detailed results of individual experiments.
@article{arxiv.2501.05984,
title = {The Safe Trusted Autonomy for Responsible Space Program},
author = {Kerianne L. Hobbs and Sean Phillips and Michelle Simon and Joseph B. Lyons and Jared Culbertson and Hamilton Scott Clouse and Nathaniel Hamilton and Kyle Dunlap and Zachary S. Lippay and Joshua Aurand and Zachary I. Bell and Taleri Hammack and Dorothy Ayres and Rizza Lim},
journal= {arXiv preprint arXiv:2501.05984},
year = {2025}
}