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Author

Zebin Huang

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Open access Aug 2026

ODARRL: Obstacle- and Disturbance-Aware End-to-End Residual Reinforcement Learning for Underwater Robot Trajectory Tracking with Obstacle Avoidance

ODARRL is proposed, an obstacle- and disturbance-aware sensor-to-thruster (ST) end-to-end residual reinforcement learning framework for safe trajectory execution of underwater robots.

Ling-Han Meng, Zebin Huang, Qing-Feng Yao et al. · 1 citation

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