{"ID":2891942,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16941","arxiv_id":"2507.16941","title":"Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection","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.","short_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 usi...","url_abs":"https://arxiv.org/abs/2507.16941","url_pdf":"https://arxiv.org/pdf/2507.16941v1","authors":"[\"Daniel Correa\",\"Tero Kaarlela\",\"Jose Fuentes\",\"Paulo Padrao\",\"Alain Duran\",\"Leonardo Bobadilla\"]","published":"2025-07-22T18:24:23Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}