用游戏引擎+强化学习，让机器人零样本完成珊瑚采样。
Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection
- 用数字孪生和游戏引擎构建仿真环境，训练水下机器人采样智能体。
- 通过实时水下运动捕捉系统实现虚拟与物理世界的精准同步验证。
- 首次融合游戏引擎、深度强化学习与水下动捕，实现零样本迁移。
本文提出一种用于开发自主水下机器人珊瑚采样智能体的强化学习（RL）环境，该任务对珊瑚礁保护与研究至关重要。通过软件在环（SIL）和硬件在环（HIL）方式，利用数字孪生（DT）在仿真中训练人工智能（AI）控制器，并在物理实验中进行验证。水下运动捕捉（MOCAP）系统在验证测试中提供实时3D位置与姿态反馈，确保数字与物理域间的精确同步。该方法的关键创新在于结合通用游戏引擎进行仿真、深度强化学习以及实时水下运动捕捉，实现高效的零样本模拟到现实迁移策略。
原文摘要 · Abstract (English)
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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