Abstract: Enabling autonomous robots to navigate, explore, and map underwater caves safely and efficiently is of significant importance to marine robotics and archaeology. In this work, we demonstrate the system design and algorithmic integration of a visual servoing capability for semantically guided autonomous underwater cave exploration. We present the hardware and edge-AI design considerations to enable this feature on a novel 6-DOF robot named CavePI. The guided navigation is driven by a computationally light yet robust AI-based perception module, delivering a rich semantic understanding of the environment. Subsequently, a robust control mechanism enables CavePI to track the semantic guides and navigate inside complex cave environments. We evaluate the CavePI system through field experiments in natural underwater caves and spring-water sites, and further validate its ROS (Robot Operating System)-based digital twin in a simulation environment. Our results highlight how these integrated design choices facilitate reliable navigation under feature-deprived, GPS-denied, and low-visibility conditions. The system design, code, and data are available on the project website: truncated for blind review.