OceanGym: A Benchmark Environment for Underwater Embodied Agents
Authors: name: Yida Xue, name: Mingjun Mao, name: Xiangyuan Ru, name: Yuqi Zhu, name: Baochang Ren, name: Shuofei Qiao, name: Mengru Wang, name: Shumin Deng, name: Xinyu An, name: Ningyu Zhang, name: Ying Chen, name: Huajun Chen
Organization: Zhejiang University

We introduce OceanGym, the first comprehensive benchmark for ocean underwater
embodied agents, designed to advance AI in one of the most demanding real-world
environments. Unlike terrestrial or aerial domains, underwater settings present
extreme perceptual and decision-making challenges, including low visibility,
dynamic ocean currents, making effective agent deployment exceptionally
difficult. OceanGym encompasses eight realistic task domains and a unified
agent framework driven by Multi-modal Large Language Models (MLLMs), which
integrates perception, memory, and sequential decision-making. Agents are
required to comprehend optical and sonar data, autonomously explore complex
environments, and accomplish long-horizon objectives under these harsh
conditions. Extensive experiments reveal substantial gaps between
state-of-the-art MLLM-driven agents and human experts, highlighting the
persistent difficulty of perception, planning, and adaptability in ocean
underwater environments. By providing a high-fidelity, rigorously designed
platform, OceanGym establishes a testbed for developing robust embodied AI and
transferring these capabilities to real-world autonomous ocean underwater
vehicles, marking a decisive step toward intelligent agents capable of
operating in one of Earth's last unexplored frontiers. The code and data are
available at https://github.com/OceanGPT/OceanGym.