Tag Map: A Text-Based Map for Spatial Reasoning and Navigation with Large Language Models
This addresses the need for efficient, LLM-integrated maps in robotics, offering a domain-specific improvement over existing implicit methods.
The paper tackles the problem of providing scene context to large language models (LLMs) for robot task planning by proposing an explicit text-based map that represents thousands of semantic classes, which localizes entities comparably to open vocabulary maps while using two to four orders of magnitude less memory.
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.