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

2024-09-16

arXiv.org

AI Summary

To address the challenge of agile and efficient locomotion control in biomimetic robotic fish, this paper proposes a CPG-free end-to-end deep reinforcement learning framework that directly maps sensory inputs to actuator commands. Methodologically, it integrates high-fidelity CFD-based simulation modeling with a dual calibration mechanism—standardizing fluid density and servo dynamics—to enable zero-shot sim-to-real deployment. Key contributions include: (i) the first CPG-free, purely data-driven swimming control paradigm; and (ii) a transferable dual-calibration strategy that substantially narrows the simulation-to-reality gap. Experimental validation on a physical robotic fish platform demonstrates a 18% increase in swimming speed, a 32% reduction in turning radius, and a 24% decrease in energy consumption, while enabling open-loop execution of complex underwater maneuvers.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Low Level & Physics-based Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Underwater Robotics
• Biomimetic Fish
• Efficient Swimming

Innovation

• Deep Reinforcement Learning
• Autonomous Swimming
• Adaptive Underwater Environment