An RL policy trained in simulation with randomized vessel dynamics and conditioned on history via a teacher-student latent module achieves zero-shot real deployment on two ASVs, outperforming non-adaptive baselines by up to 58% in position error.
Experimental evaluation of an au- tonomous surface vehicle for water quality and greenhouse gas emis- sion monitoring,
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Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
An RL policy trained in simulation with randomized vessel dynamics and conditioned on history via a teacher-student latent module achieves zero-shot real deployment on two ASVs, outperforming non-adaptive baselines by up to 58% in position error.