# Learning Agile Swimming: An End-to-End Approach without CPGs

arXiv: https://arxiv.org/abs/2409.10019 · HTML: https://mlsanity.com/paper/2409.10019/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles](https://mlsanity.com/paper/2607.09557/) (2026)

## Abstract (arXiv)

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 constraints, 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 calibration and servo response calibration, 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 turn-around radii, and reduced energy consumption compared to the state-of-the-art swimming controllers. Furthermore, the proposed framework shows promise in addressing complex tasks, paving the way for more effective deployment of robotic fish in real aquatic environments.