@inproceedings{chen-etal-2024-mapgpt,
title = "{M}ap{GPT}: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation",
author = "Chen, Jiaqi and
Lin, Bingqian and
Xu, Ran and
Chai, Zhenhua and
Liang, Xiaodan and
Wong, Kwan-Yee",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.529",
doi = "10.18653/v1/2024.acl-long.529",
pages = "9796--9810",
abstract = "Embodied agents equipped with GPT as their brain have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt the GPT-4 to select potential locations within localized environments, without constructing an effective {``}global-view{''} for the agent to understand the overall environment. In this work, we present a novel **map**-guided **GPT**-based agent, dubbed **MapGPT**, which introduces an online linguistic-formed map to encourage the global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on the R2R and REVERIE simultaneously ({\textasciitilde}10{\%} and {\textasciitilde}12{\%} improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT.",
}
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<abstract>Embodied agents equipped with GPT as their brain have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt the GPT-4 to select potential locations within localized environments, without constructing an effective “global-view” for the agent to understand the overall environment. In this work, we present a novel **map**-guided **GPT**-based agent, dubbed **MapGPT**, which introduces an online linguistic-formed map to encourage the global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on the R2R and REVERIE simultaneously (~10% and ~12% improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT.</abstract>
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%0 Conference Proceedings
%T MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation
%A Chen, Jiaqi
%A Lin, Bingqian
%A Xu, Ran
%A Chai, Zhenhua
%A Liang, Xiaodan
%A Wong, Kwan-Yee
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F chen-etal-2024-mapgpt
%X Embodied agents equipped with GPT as their brain have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt the GPT-4 to select potential locations within localized environments, without constructing an effective “global-view” for the agent to understand the overall environment. In this work, we present a novel **map**-guided **GPT**-based agent, dubbed **MapGPT**, which introduces an online linguistic-formed map to encourage the global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on the R2R and REVERIE simultaneously (~10% and ~12% improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT.
%R 10.18653/v1/2024.acl-long.529
%U https://aclanthology.org/2024.acl-long.529
%U https://doi.org/10.18653/v1/2024.acl-long.529
%P 9796-9810
Markdown (Informal)
[MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation](https://aclanthology.org/2024.acl-long.529) (Chen et al., ACL 2024)
ACL