  Embodied agents equipped with GPT as their brains have exhibited
extraordinary decision-making and generalization abilities across various
tasks. However, existing zero-shot agents for vision-and-language navigation
(VLN) only prompt 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 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 R2R and REVERIE
simultaneously (~10% and ~12% improvements in SR), and showcasing the newly
emergent global thinking and path planning abilities of the GPT.
