Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16941 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912497886298112 |
|---|---|
| author | Correa, Daniel Kaarlela, Tero Fuentes, Jose Padrao, Paulo Duran, Alain Bobadilla, Leonardo |
| author_facet | Correa, Daniel Kaarlela, Tero Fuentes, Jose Padrao, Paulo Duran, Alain Bobadilla, Leonardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection Correa, Daniel Kaarlela, Tero Fuentes, Jose Padrao, Paulo Duran, Alain Bobadilla, Leonardo Robotics 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. |
| title | Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.16941 |