  In this paper, a method for generating a map from path information described
using natural language (textual path) is proposed. In recent years, robotics
research mainly focus on vision-and-language navigation (VLN), a navigation
task based on images and textual paths. Although VLN is expected to facilitate
user instructions to robots, its current implementation requires users to
explain the details of the path for each navigation session, which results in
high explanation costs for users. To solve this problem, we proposed a method
that creates a map as a topological map from a textual path and automatically
creates a new path using this map. We believe that large language models (LLMs)
can be used to understand textual path. Therefore, we propose and evaluate two
methods, one for storing implicit maps in LLMs, and the other for generating
explicit maps using LLMs. The implicit map is in the LLM's memory. It is
created using prompts. In the explicit map, a topological map composed of nodes
and edges is constructed and the actions at each node are stored. This makes it
possible to estimate the path and actions at waypoints on an undescribed path,
if enough information is available. Experimental results on path instructions
generated in a real environment demonstrate that generating explicit maps
achieves significantly higher accuracy than storing implicit maps in the LLMs.
