Guide-LLM: 視覚障害者を誘導する身体性を持つLLMエージェントとテキストベースのトポロジカルマップ
Guide-LLM: An Embodied LLM Agent and Text-Based Topological Map for Robotic Guidance of People with Visual Impairments
視覚障害者の屋内ナビゲーション支援のため、テキストベースのトポロジカルマップとLLMの常識推論を組み合わせ、危険検知や個人の好みに応じた経路計画を行う身体性エージェントを提案した。
著者: Sangmim Song, Sarath Kodagoda, Amal Gunatilake, Marc G. Carmichael, Karthick Thiyagarajan, Jodi Martin
分類: cs.RO, cs.AI, cs.CL
原文アブストラクト
Navigation presents a significant challenge for persons with visual impairments (PVI). While traditional aids such as white canes and guide dogs are invaluable, they fall short in delivering detailed spatial information and precise guidance to desired locations. Recent developments in large language models (LLMs) and vision-language models (VLMs) offer new avenues for enhancing assistive navigation. In this paper, we introduce Guide-LLM, an embodied LLM-based agent designed to assist PVI in navigating large indoor environments. Our approach features a novel text-based topological map that enables the LLM to plan global paths using a simplified environmental representation, focusing on straight paths and right-angle turns to facilitate navigation. Additionally, we utilize the LLM's commonsense reasoning for hazard detection and personalized path planning based on user preferences. Simulated experiments demonstrate the system's efficacy in guiding PVI, underscoring its potential as a significant advancement in assistive technology. The results highlight Guide-LLM's ability to offer efficient, adaptive, and personalized navigation assistance, pointing to promising advancements in this field.
