@inproceedings{xue-etal-2026-oceangym,
title = "{O}cean{G}ym: Evaluating Language-Grounded Embodied Agents in Underwater Environments",
author = "Xue, Yida and
Mao, Mingjun and
Ru, Xiangyuan and
Zhu, Yuqi and
Ren, Baochang and
Qiao, Shuofei and
Wang, Mengru and
Deng, Shumin and
An, Xinyu and
Zhang, Ningyu and
Zheng, Guozhou and
Chen, Ying and
Chen, Huajun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.984/",
pages = "22678--22699",
ISBN = "979-8-89176-459-0",
abstract = "We introduce OceanGym, the first comprehensive benchmark for underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. OceanGym features eight realistic task domains and a unified agent framework driven by Multimodal Large Language Models (MLLMs) that integrates perception, memory, and sequential decision-making. Agents must follow natural language instructions, interpret optical and sonar inputs, autonomously explore complex environments, and achieve long-horizon goals. Extensive experiments reveal a significant performance gap between state-of-the-art MLLM-driven agents and human experts, underscoring persistent challenges in perception, planning, and adaptation underwater. By offering a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for robust embodied AI and represents a foundational step toward sim-to-real transfer on Autonomous Underwater Vehicles (AUVs), marking progress toward agents capable of operating in one of Earth{'}s last unexplored frontiers."
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<abstract>We introduce OceanGym, the first comprehensive benchmark for underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. OceanGym features eight realistic task domains and a unified agent framework driven by Multimodal Large Language Models (MLLMs) that integrates perception, memory, and sequential decision-making. Agents must follow natural language instructions, interpret optical and sonar inputs, autonomously explore complex environments, and achieve long-horizon goals. Extensive experiments reveal a significant performance gap between state-of-the-art MLLM-driven agents and human experts, underscoring persistent challenges in perception, planning, and adaptation underwater. By offering a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for robust embodied AI and represents a foundational step toward sim-to-real transfer on Autonomous Underwater Vehicles (AUVs), marking progress toward agents capable of operating in one of Earth’s last unexplored frontiers.</abstract>
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%0 Conference Proceedings
%T OceanGym: Evaluating Language-Grounded Embodied Agents in Underwater Environments
%A Xue, Yida
%A Mao, Mingjun
%A Ru, Xiangyuan
%A Zhu, Yuqi
%A Ren, Baochang
%A Qiao, Shuofei
%A Wang, Mengru
%A Deng, Shumin
%A An, Xinyu
%A Zhang, Ningyu
%A Zheng, Guozhou
%A Chen, Ying
%A Chen, Huajun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xue-etal-2026-oceangym
%X We introduce OceanGym, the first comprehensive benchmark for underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. OceanGym features eight realistic task domains and a unified agent framework driven by Multimodal Large Language Models (MLLMs) that integrates perception, memory, and sequential decision-making. Agents must follow natural language instructions, interpret optical and sonar inputs, autonomously explore complex environments, and achieve long-horizon goals. Extensive experiments reveal a significant performance gap between state-of-the-art MLLM-driven agents and human experts, underscoring persistent challenges in perception, planning, and adaptation underwater. By offering a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for robust embodied AI and represents a foundational step toward sim-to-real transfer on Autonomous Underwater Vehicles (AUVs), marking progress toward agents capable of operating in one of Earth’s last unexplored frontiers.
%U https://aclanthology.org/2026.emnlp-main.984/
%P 22678-22699
Markdown (Informal)
[OceanGym: Evaluating Language-Grounded Embodied Agents in Underwater Environments](https://aclanthology.org/2026.emnlp-main.984/) (Xue et al., EMNLP 2026)
ACL
- Yida Xue, Mingjun Mao, Xiangyuan Ru, Yuqi Zhu, Baochang Ren, Shuofei Qiao, Mengru Wang, Shumin Deng, Xinyu An, Ningyu Zhang, Guozhou Zheng, Ying Chen, and Huajun Chen. 2026. OceanGym: Evaluating Language-Grounded Embodied Agents in Underwater Environments. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 22678–22699, Budapest, Hungary. Association for Computational Linguistics.