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, an obstacle- and disturbance-aware sensor-to-thruster (ST) end-to-end residual reinforcement learning framewo...

Using cable transmission in underwater manipulators helps to reduce the mass and rotational inertia of distal moving components, but the control performance of cable-driven joints is affected by flexible cable transmission, equivalent joint-side friction, hydrodynamic effects, and external disturbances. This paper proposes a control method combining time-delay estimation (TDE) with gain-scheduled sliding mode control (GSMC) fo...

With the continuous advancement of marine development, underwater operational tasks are becoming increasingly diverse and complex. Addressing the limitations of traditional methods and intelligent planning—which focus solely on acquiring task skills while separating grasp planning from force planning—this paper proposes a modeling approach integrating impedance control with deep reinforcement learning. Using a five-finger huma...