OceanGym: A Benchmark Environment for Underwater Embodied Agents
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.
68dc947d4159d1f2418f9af3
Yida Xue
68dc947d4159d1f2418f9af4
Mingjun Mao
68dc947d4159d1f2418f9af5
Xiangyuan Ru
68dc947d4159d1f2418f9af6
Yuqi Zhu
68dc947d4159d1f2418f9af7
Baochang Ren
68dc947d4159d1f2418f9af8
Shuofei Qiao
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Mengru Wang
68dc947d4159d1f2418f9afa
Shumin Deng
68dc947d4159d1f2418f9afb
Xinyu An
68dc947d4159d1f2418f9afc
Ningyu Zhang
claimed_verified
2025-10-01T10:21:42.160Z
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/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
68dc947d4159d1f2418f9afd
Ying Chen
68dc947d4159d1f2418f9afe
Huajun Chen
OceanGym: A Benchmark Environment for Underwater Embodied Agents
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.
OceanGym is a benchmark for underwater embodied agents using Multi-modal Large Language Models to address challenges in perception, planning, and adaptability in harsh ocean environments.
Multi-modal Large Language Models MLLMs optical data sonar data sequential decision-making embodied AI autonomous ocean underwater vehicles