RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets

TL;DR

RL-AVIST introduces a reinforcement learning framework for autonomous visual inspection of space targets, demonstrating improved trajectory control and generalization in simulated space scenarios, advancing autonomous spacecraft capabilities.

Contribution

The paper presents RL-AVIST, a novel RL-based approach utilizing high-fidelity simulation and model-based RL for autonomous space target inspection, addressing adaptability and robustness challenges.

Findings

Model-based RL improves trajectory fidelity and sample efficiency.

Generalized agents perform well across diverse space scenarios.

Policies demonstrate robustness across different spacecraft designs.

Abstract

The growing need for autonomous on-orbit services such as inspection, maintenance, and situational awareness calls for intelligent spacecraft capable of complex maneuvers around large orbital targets. Traditional control systems often fall short in adaptability, especially under model uncertainties, multi-spacecraft configurations, or dynamically evolving mission contexts. This paper introduces RL-AVIST, a Reinforcement Learning framework for Autonomous Visual Inspection of Space Targets. Leveraging the Space Robotics Bench (SRB), we simulate high-fidelity 6-DOF spacecraft dynamics and train agents using DreamerV3, a state-of-the-art model-based RL algorithm, with PPO and TD3 as model-free baselines. Our investigation focuses on 3D proximity maneuvering tasks around targets such as the Lunar Gateway and other space assets. We evaluate task performance under two complementary regimes: generalized agents trained on randomized velocity vectors, and specialized agents trained to follow fixed trajectories emulating known inspection orbits. Furthermore, we assess the robustness and generalization of policies across multiple spacecraft morphologies and mission domains. Results demonstrate that model-based RL offers promising capabilities in trajectory fidelity, and sample efficiency, paving the way for scalable, retrainable control solutions for future space operations