Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
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.
Key facts. Cited by 1 paper 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.
Builds on
14 of 32 references.
References · 14
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
- 2019Control Barrier Functions: Theory and Applicationscited by 372
- 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
- 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
- 2023Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illuminationcited by 8
- 2021Guaranteed Safe Spacecraft Docking with Control Barrier Functionscited by 5
- 2021Comparing Run Time Assurance Approaches for Safe Spacecraft Dockingcited by 5
- 2022Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agentscited by 5
- 2023Run Time Assurance for Autonomous Spacecraft Inspectioncited by 5
- 2023Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learningcited by 4
- 2022Control Barrier Functions for Systems with Multiple Control Inputscited by 3
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Citing papers per year: 2026: 1.
1 paper.
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- 2024Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles
- 2024Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies
- 2024A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
- 2025RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
- 2025Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems
- 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
- 2025Toward Trusted Onboard AI: Advancing Small Satellite Operations using Reinforcement Learning
- 2026RL-STPA: Adapting System-Theoretic Hazard Analysis for Safety-Critical Reinforcement Learning