OceanGym: A Benchmark Environment for Underwater Embodied Agents
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Computer Science > Computation and Language
arXiv:2509.26536 (cs)
[Submitted on 30 Sep 2025 (v1), last revised 28 Aug 2026 (this version, v3)]
Title:OceanGym: A Benchmark Environment for Underwater Embodied Agents
Authors:Yida Xue, Mingjun Mao, Xiangyuan Ru, Yuqi Zhu, Baochang Ren, Shuofei Qiao, Mengru Wang, Shumin Deng, Xinyu An, Ningyu Zhang, Ying Chen, Huajun Chen
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Abstract: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 this https URL.
| Comments: | EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO) |
| Cite as: | arXiv:2509.26536 [cs.CL] |
| (or arXiv:2509.26536v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2509.26536
arXiv-issued DOI via DataCite
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Submission history
From: Ningyu Zhang [view email][v1] Tue, 30 Sep 2025 17:09:32 UTC (15,215 KB)
[v2] Tue, 25 Nov 2025 15:21:05 UTC (15,733 KB)
[v3] Fri, 28 Aug 2026 03:26:18 UTC (16,876 KB)
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