Search papers, labs, and topics across Lattice.
The paper introduces MarineGym, a high-performance reinforcement learning platform for underwater robotics built on Isaac Sim with a novel GPU-accelerated hydrodynamic plugin. This platform achieves a rollout speed of 250,000 FPS on a single RTX 3060, significantly improving training efficiency compared to existing underwater simulation environments. The platform facilitates Sim2Real transfer through a domain randomization toolkit and provides standardized benchmarks for core underwater control tasks using multiple UUV models and propulsion systems.
Train underwater robots 10x faster: MarineGym hits 250,000 frames per second on a single RTX 3060, blowing past existing simulation platforms.
This study introduces MarineGym, a high-performance reinforcement learning platform tailored for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of reinforcement learning 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, multiple propulsion systems, and a set of predefined tasks covering core underwater control challenges. Additionally, the domain randomization toolkit allows flexible adjustments of the simulation and task parameters during training to improve the Sim2Real transfer. Further benchmark experiments demonstrate that MarineGym improves training efficiency over existing platforms and supports robust policy adaptation under various perturbations in the marine environment. We expect this platform to 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/.
