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.
@article{arxiv.2507.16941,
title = {Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection},
author = {Daniel Correa and Tero Kaarlela and Jose Fuentes and Paulo Padrao and Alain Duran and Leonardo Bobadilla},
journal= {arXiv preprint arXiv:2507.16941},
year = {2025}
}