Learning Agile Swimming: An End-to-End Approach without CPGs
<|reference_start|>Learning Agile Swimming: An End-to-End Approach without CPGs: The pursuit of agile and efficient underwater robots, especially bio-mimetic robotic fish, has been impeded by challenges in creating motion controllers that are able to fully exploit their hydrodynamic capabilities. This paper addresses these challenges by introducing a novel, model-free, end-to-end control framework that leverages Deep Reinforcement Learning (DRL) to enable agile and energy-efficient swimming of robotic fish. Unlike existing methods that rely on predefined trigonometric swimming patterns like Central Pattern Generators (CPG), our approach directly outputs low-level actuator commands without strong constraint, enabling the robotic fish to learn agile swimming behaviors. In addition, by integrating a high-performance Computational Fluid Dynamics (CFD) simulator with innovative sim-to-real strategies, such as normalized density matching and servo response matching, the proposed framework significantly mitigates the sim-to-real gap, facilitating direct transfer of control policies to real-world environments without fine-tuning. Comparative experiments demonstrate that our method achieves faster swimming speeds, smaller turning radii, and reduced energy consumption compared to the conventional CPG-PID-based controllers. Furthermore, the proposed framework shows promise in addressing complex tasks in diverse scenario, paving the way for more effective deployment of robotic fish in real aquatic environments.<|reference_end|>
@article{lin2024learning,
    title={Learning Agile Swimming: An End-to-End Approach without CPGs},
    author={Xiaozhu Lin, Xiaopei Liu, Yang Wang},
    journal={arXiv preprint arXiv:2409.10019},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10019},
    primaryClass={cs.RO}
}