Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

• URL: http://arxiv.org/abs/2607.02037v1
• Date: Thu, 02 Jul 2026 11:04:21 GMT
• Title: Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
• Abstract summary: We present an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment.<n>The policy is trained in simulation under randomized vessel dynamics and is deployed zero-shot to two real-world platforms without any fine-tuning.<n>In real-world experiments on two different platforms, the adaptive policy outperforms non-adaptive learning-based baselines by up to 58% in position mean absolute error.
• Score: 3.8494315501944736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment using a single policy. Since the deployment platform's dynamics are unknown to the policy, we address cross-platform generalization with the standard partial-observability approach of conditioning on interaction history, employing a teacher-student architecture in which a learned module infers a latent representation of the platform dynamics. The policy is trained in simulation under randomized vessel dynamics and is deployed zero-shot to two real-world platforms without any fine-tuning, despite relying on a simple analytical dynamics model rather than a high-fidelity hydrodynamic simulator. In real-world experiments on two different platforms, the adaptive policy outperforms non-adaptive learning-based baselines by up to 58% in position mean absolute error while approaching the tracking accuracy of a platform-specific tuned controller.

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