視覚障害者支援ロボットのためのLLMによる障害物回避ナビゲーション説明
Navigation Explainability for Object Avoidance with LLMs for Assistive Robots
盲導犬ロボットの障害物回避動作を大規模言語モデルで自然言語説明する枠組みを提案し、Unitree Go2で実装して説明の文脈構成が信頼と満足度に与える影響を評価した。
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著者: Courtney Young, Yi Lian, Taery Kim, Sehoon Ha
分類: cs.RO
原文アブストラクト
Blind and visually impaired individuals face uncertainty when navigating unfamiliar areas. Whether a cane or a guide dog is used for navigation, this may prove to be challenging. To mitigate this challenge, we present the Context-Aware Navigation Explanation framework that explains guide dog movements for obstacle avoidance and navigation using a large language model. This system integrates perceptual context with planned future trajectories to generate natural-language explanations of robot movement. We deploy the system on a Unitree Go2 platform in indoor navigation scenarios and evaluate how different explanation context configurations affect user trust and explanation satisfaction. Results show a strong positive relationship between explanation satisfaction and trust, and suggest that combining obstacle information with future trajectory leads to the most effective explanations. These findings highlight the potential of context-aware language explanations to improve transparency and trust in assistive robot navigation.