Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
This work addresses the problem of deploying a single control policy across multiple autonomous surface vehicles with different dynamics, which is important for reducing engineering effort in multi-platform operations.
The paper presents an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment of autonomous surface vehicles. In real-world experiments on two different platforms, the adaptive policy outperforms non-adaptive baselines by up to 58% in position error while approaching platform-specific tuned controller accuracy.
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.