# Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/2405.06770 · HTML: https://mlsanity.com/paper/2405.06770/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 10 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems](https://mlsanity.com/paper/1705.01292/) (2017)
- [Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems](https://mlsanity.com/paper/2110.03506/) (2021)
- [Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles](https://mlsanity.com/paper/1812.06120/) (2018)
- [Safe Control Synthesis via Input Constrained Control Barrier Functions](https://mlsanity.com/paper/2104.01704/) (2021)
- [Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination](https://mlsanity.com/paper/2308.02743/) (2023)
- [Guaranteed Safe Spacecraft Docking with Control Barrier Functions](https://mlsanity.com/paper/2109.07027/) (2021)
- [Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents](https://mlsanity.com/paper/2207.04117/) (2022)
- [Run Time Assurance for Autonomous Spacecraft Inspection](https://mlsanity.com/paper/2302.02885/) (2023)
- [Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning](https://mlsanity.com/paper/2302.14188/) (2023)
- [Composition of Safety Constraints For Fixed-Wing Collision Avoidance Amidst Limited Communications](https://mlsanity.com/paper/1906.03771/) (2019)
- [Space Trusted Autonomy Readiness Levels](https://mlsanity.com/paper/2210.09059/) (2022)
- [Run Time Assurance for Simultaneous Constraint Satisfaction During Spacecraft Attitude Maneuvering](https://mlsanity.com/paper/2402.14723/) (2024)

## Built on by (most cited first, 2 of 2)

- [Cognitively-Grounded On-Device Runtime Learning for Ground Robots in Unknown Physical Environments](https://mlsanity.com/paper/2608.28677/) (2026)
- [SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking](https://mlsanity.com/paper/2609.06061/) (2026)

## Abstract (arXiv)

On-orbit spacecraft inspection is an important capability for enabling servicing and manufacturing missions and extending the life of spacecraft. However, as space operations become increasingly more common and complex, autonomous control methods are needed to reduce the burden on operators to individually monitor each mission. In order for autonomous control methods to be used in space, they must exhibit safe behavior that demonstrates robustness to real world disturbances and uncertainty. In this paper, neural network controllers (NNCs) trained with reinforcement learning are used to solve an inspection task, which is a foundational capability for servicing missions. Run time assurance (RTA) is used to assure safety of the NNC in real time, enforcing several different constraints on position and velocity. The NNC and RTA are tested in the real world using unmanned aerial vehicles designed to emulate spacecraft dynamics. The results show this emulation is a useful demonstration of the capability of the NNC and RTA, and the algorithms demonstrate robustness to real world disturbances.