適応的強化学習による自律水上艇のクロスプラットフォーム制御
Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
単一のポリシーで異なる水上艇にゼロショット適用可能な適応的強化学習手法を提案し、実機2機種で非適応ベースラインを最大58%上回る追従精度を達成した。
詳しい要約
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著者: Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran
分類: cs.RO, cs.LG
原文アブストラクト
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.
