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

本文提出了一种新型的地图引导 GPT-based 路径规划代理模型 (MapGPT)，用于零 - shot 视觉语言导航任务，通过在线构建拓扑地图转换为提示来引导全局探索，要求代理模型明确输出和更新多步路径规划以避免陷入局部探索，并在 R2R 和 REVERIE 数据集上取得了令人印象深刻的性能 (分别为 38.8% 和 28.4% 的成功率)，展示了 GPT 模型在全局思考和路径规划能力方面的新兴能力，与以往需要微调独立参数或特定提示设计来满足不同数据集中不同指令风格的 VLN 代理相比，我们的 MapGPT 更统一，能够在不同指令风格之间无缝适应，这在该领域是首次。