Qingfeng Yao

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

ROVs are essential for marine exploration and underwater operations, yet conventional teleoperation relies heavily on skilled human operators, and many autonomous methods stop at high-level planning rather than low-level actuation, limiting robustness in disturbed and cluttered environments. This paper proposes ODARRL,...