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arXiv · 2507.16941

Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection

Daniel Correa·Tero Kaarlela·Jose Fuentes·Paulo Padrao·Alain Duran·Leonardo Bobadilla
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Abstract

This paper presents a reinforcement learning (RL) environment for developing an autonomous underwater robotic coral sampling agent, a crucial coral reef conservation and research task. Using software-in-the-loop (SIL) and hardware-in-the-loop (HIL), an RL-trained artificial intelligence (AI) controller is developed using a digital twin (DT) in simulation and subsequently verified in physical experiments. An underwater motion capture (MOCAP) system provides real-time 3D position and orientation feedback during verification testing for precise synchronization between the digital and physical domains. A key novelty of this approach is the combined use of a general-purpose game engine for simulation, deep RL, and real-time underwater motion capture for an effective zero-shot sim-to-real strategy.

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Daniel Correa, Tero Kaarlela, Jose Fuentes, Paulo Padrao, Alain Duran, Leonardo Bobadilla. 2025-07-22. Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection. https://arxiv.org/abs/2507.16941

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