mlsanityData to 14 Oct 2026

Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments

arXiv 2502.20554 ↗First posted 27 Feb 2025cs.RO cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

With the increasingly congested and contested space environment, safe and effective satellite operation has become increasingly challenging. As a result, there is growing interest in autonomous satellite capabilities, with common machine learning techniques gaining attention for their potential to address complex decision-making in the space domain. However, the "black-box" nature of many of these methods results in difficulty understanding the model's input/output relationship and more specifically its sensitivity to environmental disturbances, sensor noise, and control intervention. This paper explores the use of Deep Reinforcement Learning (DRL) for satellite control in multi-agent inspection tasks. The Local Intelligent Network of Collaborative Satellites (LINCS) Lab is used to test the performance of these control algorithms across different environments, from simulations to real-world quadrotor UAV hardware, with a particular focus on understanding their behavior and potential degradation in performance when deployed beyond the training environment.

Key facts. No citing papers in the map yet (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 10 of its 27 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

10 of 27 references.

References · 10

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  2. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  3. 2019Action Robust Reinforcement Learning and Applications in Continuous Controlcited by 174
  4. 2020Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspectivecited by 59
  5. 2013Scaling Up Robust MDPs by Reinforcement Learningcited by 17
  6. 2016Supervision via Competition: Robot Adversaries for Learning Taskscited by 16
  7. 2023Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illuminationcited by 8
  8. 2023Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learningcited by 4
  9. 2022Space Trusted Autonomy Readiness Levelscited by 2
  10. 2025Stability Analysis of Deep Reinforcement Learning for Multi-Agent Inspection in a Terrestrial Testbedcited by 1

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  1. 2024Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection
  2. 2024Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing
  3. 2024Continual Deep Reinforcement Learning for Decentralized Satellite Routing
  4. 2024Satellite Chasers: Divergent Adversarial Reinforcement Learning to Engage Intelligent Adversaries on Orbit
  5. 2025Deep Reinforcement Learning Policies for Underactuated Satellite Attitude Control
  6. 2025Toward Trusted Onboard AI: Advancing Small Satellite Operations using Reinforcement Learning
  7. 2025RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
  8. 2025LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller