TLDR: OCEANGYM is the first comprehensive benchmark environment for training and evaluating AI agents in challenging underwater settings. It features realistic simulations, diverse tasks, and a multi-modal Large Language Model (MLLM)-driven agent framework. Experiments reveal significant performance gaps between current AI models and human experts, particularly in low visibility and sonar interpretation, highlighting the need for further research in robust perception, reasoning, and control for autonomous underwater vehicles.
The vast and mysterious underwater world presents some of the most formidable challenges for artificial intelligence. Unlike land or air environments, the ocean’s depths are characterized by low visibility, dynamic currents, and complex terrains, making the deployment of autonomous agents exceptionally difficult. To address this, researchers have introduced a groundbreaking new platform called OCEANGYM, the first comprehensive benchmark environment specifically designed for underwater embodied agents.
Navigating the Ocean’s Depths with AI
Developed by Yida Xue, Mingjun Mao, Xiangyuan Ru, Yuqi Zhu, Baochang Ren, Shuofei Qiao, Mengru Wang, Shumin Deng, Xinyu An, Ningyu Zhang, Ying Chen, and Huajun Chen, OCEANGYM aims to push the boundaries of AI in these demanding real-world settings. The platform provides a high-fidelity, rigorously designed testbed for developing robust embodied AI and transferring these capabilities to real-world autonomous underwater vehicles (AUVs).
The core of OCEANGYM encompasses eight realistic task domains and a unified agent framework powered by Multi-modal Large Language Models (MLLMs). These agents are tasked with integrating perception, memory, and sequential decision-making to operate effectively. They must interpret both optical (visual) and sonar data, explore intricate environments autonomously, and achieve long-term objectives under harsh conditions.
The Unique Challenges of Underwater AI
Underwater embodied agents face distinct hurdles compared to their terrestrial or aerial counterparts. Perceptually, poor visibility and low-light conditions necessitate a heavy reliance on sonar, inertial measurements, and other sparse data modalities, complicating sensor fusion and accurate perception. Environmentally, deep-sea and offshore areas are largely unexplored, leading to unstable localization, a lack of prior knowledge, and unpredictable dynamic currents. These factors force agents to make decisions under extreme partial observability and uncertainty.
OCEANGYM is built using Unreal Engine 5.3, creating a detailed marine environment spanning approximately 800m × 800m, with adjustable depths to simulate varying lighting conditions. It features diverse semantic regions like open water, seabed plains, underwater cliffs, pipeline networks, wreckage sites, and energy infrastructure zones. These elements are modeled with realistic physical and geometric properties, including oil pipelines, chemical waste barrels, submerged shipwrecks, electrical equipment, wind turbine foundations, and aircraft debris.
How Agents Operate in OCEANGYM
The agents in OCEANGYM operate within a control-perception loop, processing task specifications, language instructions, synchronized observations (RGB and sonar images from six directions), and their memory state. They must then generate either a textual perception response for identification tasks or a control action for navigation tasks. The action space includes discrete movements like forward, backward, left, right, up, down, and rotations.
A crucial component is the agent’s memory system, which stores past information in a sliding window, helping the agent to be more resilient in dynamic and partially observable environments. This memory-augmented approach allows the agent to leverage historical data for better decision-making.
Perception and Decision Tasks
OCEANGYM features two main types of tasks:
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Perception Tasks: These evaluate an agent’s ability to interpret visual information. Multi-View Perception assesses interpretation from multiple synchronized viewpoints, while Context-based Perception focuses on understanding sequential observations gathered during navigation.
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Decision Tasks: These evaluate decision-making in continuous 3D environments. Eight scenarios, including detection tasks (e.g., searching for sunken ships or robots) and tracking tasks (e.g., pipeline inspection), are designed to reflect real-world operations. These tasks are conducted under both shallow (high illumination) and deep water (low illumination) conditions.
Key Findings and the Human-AI Gap
Extensive experiments using models like GPT-4o-mini, Gemini-2.5, Qwen2.5-VL-7B, and MiniCPM-V-4.5 revealed significant performance gaps between MLLM-driven agents and human experts. In deep water with low visibility, the decision-making success rate for MLLMs dropped dramatically to 14.8%. Agents frequently struggled with accurate sonar data interpretation, object distinction in complex scenes, and maintaining consistent strategies over long missions.
Interestingly, while human experts consistently benefited from sonar data, MLLMs showed only limited gains, even with reference sonar images. This highlights a current limitation in MLLMs’ ability to effectively comprehend sonar imagery.
The research also found that extended exploration initially improves an agent’s environmental knowledge and task performance, but eventually plateaus. This suggests inherent limitations in current MLLM perception, memory, and reasoning, as well as a lack of intrinsic curiosity for further exploration. Memory transfer, however, proved beneficial, especially cross-task transfer in challenging deep-water environments, indicating that relevant prior experiences can provide robust guidance.
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Looking Ahead
While OCEANGYM offers a versatile testbed, it has limitations, as it cannot fully replicate all real-world factors like ocean currents, salinity, marine life, and perfect sonar noise. However, it serves as a crucial platform for evaluating foundational models, synthesizing underwater simulation data, facilitating reinforcement learning, and acting as a sim-to-real bridge for deploying models on actual AUVs.
The introduction of OCEANGYM marks a decisive step toward developing intelligent agents capable of operating in one of Earth’s last unexplored frontiers, bridging the gap between simulated research and real-world deployment. For more details, you can read the full research paper here.
