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.
· Journal of Marine Science an... · 1 citation