# Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection

arXiv: https://arxiv.org/abs/2406.11795 · HTML: https://mlsanity.com/paper/2406.11795/

## Key facts

- Cited by 1 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 14 of its 32 references that are in the map.
- First posted on arXiv 17 Jun 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, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [A Lyapunov-based Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/1805.07708/) (2018)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [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)
- [Comparing Run Time Assurance Approaches for Safe Spacecraft Docking](https://mlsanity.com/paper/2110.00447/) (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)
- [Control Barrier Functions for Systems with Multiple Control Inputs](https://mlsanity.com/paper/2203.07978/) (2022)
- [A Universal Framework for Generalized Run Time Assurance with JAX Automatic Differentiation](https://mlsanity.com/paper/2209.01120/) (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, 1 of 1)

- [Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations](https://mlsanity.com/paper/2602.07335/) (2026)

## Abstract (arXiv)

The trial and error approach of reinforcement learning (RL) results in high performance across many complex tasks, but it can also lead to unsafe behavior. Run time assurance (RTA) approaches can be used to assure safety of the agent during training, allowing it to safely explore the environment. This paper investigates the application of RTA during RL training for a 6-Degree-of-Freedom spacecraft inspection task, where the agent must control its translational motion and attitude to inspect a passive chief spacecraft. Several safety constraints are developed based on position, velocity, attitude, temperature, and power of the spacecraft, and are all enforced simultaneously during training through the use of control barrier functions. This paper also explores simulating the RL agent and RTA at different frequencies to best balance training performance and safety assurance. The agent is trained with and without RTA, and the performance is compared across several metrics including inspection percentage and fuel usage.