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Embodied agents equipped with GPT as their brain have exhibited extraordinarythinking and decision-making abilities across various tasks. However, existingzero-shot agents for vision-and-language navigation (VLN) only prompt the GPTto handle excessive environmental information and select potential locationswithin localized environments, without constructing an effective''global-view'' (e.g., a commonly-used map) for the agent to understand theoverall environment. In this work, we present a novel map-guided GPT-basedpath-planning agent, dubbed MapGPT, for the zero-shot VLN task. Specifically,we convert a topological map constructed online into prompts to encouragemap-guided global exploration, and require the agent to explicitly output andupdate multi-step path planning to avoid getting stuck in local exploration.Extensive experiments demonstrate that our MapGPT is effective, achievingimpressive performance on both the R2R and REVERIE datasets (38.8% and 28.4%success rate, respectively) and showcasing the newly emerged global thinkingand path planning capabilities of the GPT model. Unlike previous VLN agents,which require separate parameters fine-tuning or specific prompt design toaccommodate various instruction styles across different datasets, our MapGPT ismore unified as it can adapt to different instruction styles seamlessly, whichis the first of its kind in this field.

MapGPT: Map-Guided Prompting for Unified Vision-and-Language Navigation