@inproceedings{wang-etal-2025-navrag,
title = "{N}av{RAG}: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented {LLM}",
author = "Wang, Zihan and
Zhu, Yaohui and
Lee, Gim Hee and
Fan, Yachun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.442/",
doi = "10.18653/v1/2025.findings-acl.442",
pages = "8430--8440",
ISBN = "979-8-89176-256-5",
abstract = "Vision-and-Language Navigation (VLN) is an essential skill for embodied agents, allowing them to navigate in 3D environments following natural language instructions. High-performance navigation models require a large amount of training data, the high cost of manually annotating data has seriously hindered this field. Therefore, some previous methods translate trajectory videos into step-by-step instructions for expanding data, but such instructions do not match well with users' communication styles that briefly describe destinations or state specific needs. Moreover, local navigation trajectories overlook global context and high-level task planning. To address these issues, we propose NavRAG, a retrieval-augmented generation (RAG) framework that generates user demand instructions for VLN. NavRAG leverages LLM to build a hierarchical scene description tree for 3D scene understanding from global layout to local details, then simulates various user roles with specific demands to retrieve from the scene tree, generating diverse instructions with LLM. We annotate over 2 million navigation instructions across 861 scenes and evaluate the data quality and navigation performance of trained models. The model trained on our NavRAG dataset achieves SOTA performance on the REVERIE benchmark."
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<abstract>Vision-and-Language Navigation (VLN) is an essential skill for embodied agents, allowing them to navigate in 3D environments following natural language instructions. High-performance navigation models require a large amount of training data, the high cost of manually annotating data has seriously hindered this field. Therefore, some previous methods translate trajectory videos into step-by-step instructions for expanding data, but such instructions do not match well with users’ communication styles that briefly describe destinations or state specific needs. Moreover, local navigation trajectories overlook global context and high-level task planning. To address these issues, we propose NavRAG, a retrieval-augmented generation (RAG) framework that generates user demand instructions for VLN. NavRAG leverages LLM to build a hierarchical scene description tree for 3D scene understanding from global layout to local details, then simulates various user roles with specific demands to retrieve from the scene tree, generating diverse instructions with LLM. We annotate over 2 million navigation instructions across 861 scenes and evaluate the data quality and navigation performance of trained models. The model trained on our NavRAG dataset achieves SOTA performance on the REVERIE benchmark.</abstract>
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%0 Conference Proceedings
%T NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM
%A Wang, Zihan
%A Zhu, Yaohui
%A Lee, Gim Hee
%A Fan, Yachun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-navrag
%X Vision-and-Language Navigation (VLN) is an essential skill for embodied agents, allowing them to navigate in 3D environments following natural language instructions. High-performance navigation models require a large amount of training data, the high cost of manually annotating data has seriously hindered this field. Therefore, some previous methods translate trajectory videos into step-by-step instructions for expanding data, but such instructions do not match well with users’ communication styles that briefly describe destinations or state specific needs. Moreover, local navigation trajectories overlook global context and high-level task planning. To address these issues, we propose NavRAG, a retrieval-augmented generation (RAG) framework that generates user demand instructions for VLN. NavRAG leverages LLM to build a hierarchical scene description tree for 3D scene understanding from global layout to local details, then simulates various user roles with specific demands to retrieve from the scene tree, generating diverse instructions with LLM. We annotate over 2 million navigation instructions across 861 scenes and evaluate the data quality and navigation performance of trained models. The model trained on our NavRAG dataset achieves SOTA performance on the REVERIE benchmark.
%R 10.18653/v1/2025.findings-acl.442
%U https://aclanthology.org/2025.findings-acl.442/
%U https://doi.org/10.18653/v1/2025.findings-acl.442
%P 8430-8440
Markdown (Informal)
[NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM](https://aclanthology.org/2025.findings-acl.442/) (Wang et al., Findings 2025)
ACL