Reinforcement Learning-Enabled Control and Design of Rigid-Link Robotic Fish: A Comprehensive Review

With the rising demand for maritime surveys of infrastructure, energy resources, and environmental conditions, autonomous robotic fish have emerged as a promising solution with their biomimetic propulsion, agile motion, efficiency, and capacity for underwater inspection, monitoring, data collection, and exploration tasks in complex aquatic environments. Inspired by fish spines, rigid-link fish robots (RLFRs), a category of robotic fish, are widely utilized in robotics research and applications. Their rigid, actuated joints enable them to reproduce the undulatory locomotion and high maneuverability of biological fishes, while the modular nature of rigid links between joints makes them cost-effective and easy to assemble. This review examines and presents recent approaches and advancements in the field of structural design, as well as Reinforcement learning (RL)-enabled controls with sensors and actuators. Existing designs are classified by joint configuration, with key structural, material, fabrication, and propulsion considerations summarized. The review highlights the use of Q-learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) algorithms for RLFR controllers, showing their impact on adaptability, motion control, and learning in dynamic hydrodynamic conditions. Technical challenges-including unstructured environments and complex fluid-body interactions-are discussed, along with future directions. This review aims to clarify current progress and identify technological gaps for advancing rigid-link robotic fish.