打造高效水下机器人强化学习平台，加速智能控制研究
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics
- 基于Isaac Sim的GPU加速流体插件，单卡每秒跑25万帧
- 内置5种无人潜航器模型与标准任务集，支持真实场景迁移
- 适合水下机器人、强化学习、仿真训练方向的研究者使用
本文提出MarineGym，一个专为水下机器人设计的高性能强化学习平台，旨在解决现有水下仿真环境在强化学习兼容性、训练效率和标准化评测方面的不足。MarineGym集成基于Isaac Sim的GPU加速流体动力学插件，在单张NVIDIA RTX 3060显卡上实现每秒25万帧的推演速度。平台提供5种无人潜水器（UUV）模型、多种推进系统及涵盖核心水下控制挑战的预设任务。此外，DR工具包支持训练过程中灵活调整仿真与任务参数，提升从仿真到现实的迁移能力。基准实验表明，MarineGym显著提升训练效率，并在多种扰动下实现稳健策略适应。我们期望该平台推动水下机器人强化学习研究的进一步发展。更多详情请访问：https://marine-gym.com/。
原文摘要 · Abstract (English)
This work presents the MarineGym, a high-performance reinforcement learning (RL) platform specifically designed for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of RL compatibility, training efficiency, and standardized benchmarking. MarineGym integrates a proposed GPU-accelerated hydrodynamic plugin based on Isaac Sim, achieving a rollout speed of 250,000 frames per second on a single NVIDIA RTX 3060 GPU. It also provides five models of unmanned underwater vehicles (UUVs), multiple propulsion systems, and a set of predefined tasks covering core underwater control challenges. Additionally, the DR toolkit allows flexible adjustments of simulation and task parameters during training to improve Sim2Real transfer. Further benchmark experiments demonstrate that MarineGym improves training efficiency over existing platforms and supports robust policy adaptation under various perturbations. We expect this platform could drive further advancements in RL research for underwater robotics. For more details about MarineGym and its applications, please visit our project page: https://marine-gym.com/.
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