Tag Map: A Text-Based Map for Spatial Reasoning and Navigation with Large Language Models
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Author / Producer
Date
2025
Publication Type
Conference Paper
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yes
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Abstract
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.
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Publication status
published
Book title
Proceedings of The 8th Conference on Robot Learning
Journal / series
Volume
270
Pages / Article No.
2120 - 2146
Publisher
PMLR
Event
8th Conference on Robot Learning (CoRL 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Scene Understanding; Grounded Navigation; Large Language Models
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
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Funding
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