A context-dependent multi-task RL policy is trained and evaluated in HoloOcean simulation to solve multiple reef monitoring tasks with claimed improvements in sample efficiency, zero-shot generalization, and robustness to water currents.
From Simulation to Reality: Deep Reinforcement Learning for Autonomous Underwater Vehicle Docking
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ROS-DESERT middleware enables depth-adaptive AUV coordination for improved acoustic connectivity, with sea trials showing packet reception gains at 1 km range but not at shorter distances.
citing papers explorer
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Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring
A context-dependent multi-task RL policy is trained and evaluated in HoloOcean simulation to solve multiple reef monitoring tasks with claimed improvements in sample efficiency, zero-shot generalization, and robustness to water currents.
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Sea Trial Validation of the ROS-DESERT Middleware with Autonomous Underwater Vehicles
ROS-DESERT middleware enables depth-adaptive AUV coordination for improved acoustic connectivity, with sea trials showing packet reception gains at 1 km range but not at shorter distances.