@inproceedings{li-etal-2026-think,
title = "Think before Go: Hierarchical Reasoning for Image-goal Navigation",
author = "Li, Pengna and
Wu, Kangyi and
Xu, Shaoqing and
Li, Fang and
Zhao, Lin and
Chen, Long and
Yang, Zhi-Xin and
Zheng, Nanning",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1352/",
pages = "29336--29357",
ISBN = "979-8-89176-390-6",
abstract = "Image-goal navigation steers an agent to a target location specified by an image in unseen environments. Existing methods primarily handle this task by learning an end-to-end navigation policy, which compares the similarities of target and observation images and directly predicts the actions. However, when the target is distant or lies in another room, such methods fail to extract informative visual cues, leading the agent to wander around. Motivated by the human cognitive principle that deliberate, high-level reasoning guides fast, reactive execution in complex tasks, we propose Hierarchical Reasoning Navigation (HRNav), a framework that decomposes image-goal navigation into high-level planning and low-level execution. In high-level planning, a vision-language model is trained on a self-collected dataset to generate a short-horizon plan, such as whether the agent should walk through the door or down the hallway. This downgrades the difficulty of the long-horizon task, making it more amenable to the execution part. In low-level execution, an online reinforcement learning policy is utilized to decide actions conditioned on the short-horizon plan. We also devise a novel Wandering Suppression Penalty (WSP) to further reduce the wandering problem. Together, these components form a hierarchical framew ork for Image-Goal Navigation. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our method."
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<abstract>Image-goal navigation steers an agent to a target location specified by an image in unseen environments. Existing methods primarily handle this task by learning an end-to-end navigation policy, which compares the similarities of target and observation images and directly predicts the actions. However, when the target is distant or lies in another room, such methods fail to extract informative visual cues, leading the agent to wander around. Motivated by the human cognitive principle that deliberate, high-level reasoning guides fast, reactive execution in complex tasks, we propose Hierarchical Reasoning Navigation (HRNav), a framework that decomposes image-goal navigation into high-level planning and low-level execution. In high-level planning, a vision-language model is trained on a self-collected dataset to generate a short-horizon plan, such as whether the agent should walk through the door or down the hallway. This downgrades the difficulty of the long-horizon task, making it more amenable to the execution part. In low-level execution, an online reinforcement learning policy is utilized to decide actions conditioned on the short-horizon plan. We also devise a novel Wandering Suppression Penalty (WSP) to further reduce the wandering problem. Together, these components form a hierarchical framew ork for Image-Goal Navigation. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our method.</abstract>
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%0 Conference Proceedings
%T Think before Go: Hierarchical Reasoning for Image-goal Navigation
%A Li, Pengna
%A Wu, Kangyi
%A Xu, Shaoqing
%A Li, Fang
%A Zhao, Lin
%A Chen, Long
%A Yang, Zhi-Xin
%A Zheng, Nanning
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-think
%X Image-goal navigation steers an agent to a target location specified by an image in unseen environments. Existing methods primarily handle this task by learning an end-to-end navigation policy, which compares the similarities of target and observation images and directly predicts the actions. However, when the target is distant or lies in another room, such methods fail to extract informative visual cues, leading the agent to wander around. Motivated by the human cognitive principle that deliberate, high-level reasoning guides fast, reactive execution in complex tasks, we propose Hierarchical Reasoning Navigation (HRNav), a framework that decomposes image-goal navigation into high-level planning and low-level execution. In high-level planning, a vision-language model is trained on a self-collected dataset to generate a short-horizon plan, such as whether the agent should walk through the door or down the hallway. This downgrades the difficulty of the long-horizon task, making it more amenable to the execution part. In low-level execution, an online reinforcement learning policy is utilized to decide actions conditioned on the short-horizon plan. We also devise a novel Wandering Suppression Penalty (WSP) to further reduce the wandering problem. Together, these components form a hierarchical framew ork for Image-Goal Navigation. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our method.
%U https://aclanthology.org/2026.acl-long.1352/
%P 29336-29357
Markdown (Informal)
[Think before Go: Hierarchical Reasoning for Image-goal Navigation](https://aclanthology.org/2026.acl-long.1352/) (Li et al., ACL 2026)
ACL
- Pengna Li, Kangyi Wu, Shaoqing Xu, Fang Li, Lin Zhao, Long Chen, Zhi-Xin Yang, and Nanning Zheng. 2026. Think before Go: Hierarchical Reasoning for Image-goal Navigation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29336–29357, San Diego, California, United States. Association for Computational Linguistics.