タグマップ：大規模言語モデルによる空間推論とナビゲーションのためのテキストベース地図
Tag Map: A Text-Based Map for Spatial Reasoning and Navigation with Large Language Models
大規模画像認識モデルを活用し、数千の意味クラスを表現できるテキストベースの地図を提案。従来の埋め込みベース地図より2〜4桁少ないメモリで同等の位置推定精度を実現し、LLMと組み合わせた実機ナビゲーションを検証した。
著者: Mike Zhang, Kaixian Qu, Vaishakh Patil, Cesar Cadena, Marco Hutter
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Large Language Models (LLM) have emerged as a tool for robots to generate task plans using common sense reasoning. For the LLM to generate actionable plans, scene context must be provided, often through a map. Recent works have shifted from explicit maps with fixed semantic classes to implicit open vocabulary maps based on queryable embeddings capable of representing any semantic class. However, embeddings cannot directly report the scene context as they are implicit, requiring further processing for LLM integration. To address this, we propose an explicit text-based map that can represent thousands of semantic classes while easily integrating with LLMs due to their text-based nature by building upon large-scale image recognition models. We study how entities in our map can be localized and show through evaluations that our text-based map localizations perform comparably to those from open vocabulary maps while using two to four orders of magnitude less memory. Real-robot experiments demonstrate the grounding of an LLM with the text-based map to solve user tasks.
