Seokyong Song

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

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