Gait Optimization for Underwater Legged Robots Using Data-Driven Hydrodynamic Modeling and Reinforcement Learning

Abstract

Precise close-contact inspections are critical in underwater environments, where complex dynamics and biofouling present significant challenges for conventional vehicles. To address these issues, this study proposes a Reinforcement Learning (RL)-based framework to optimize the gait of an underwater legged robot for accurate and stable locomotion. A simulation environment was developed by modeling buoyancy, added mass, seabed interactions, and data-driven leg hydrodynamic forces. The control framework was designed for close-contact inspections by using a structured action space and stability-promoting rewards. The trained policy was validated through both simulation and real-world experiments, demonstrating effective mitigation of hydrodynamic disturbances. Results showed reduced pitch and altitude oscillations during high-speed forward walking and improved heading accuracy in curved trajectories.