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arXiv · 2610.28920

Navigation Explainability for Object Avoidance with LLMs for Assistive Robots

Courtney Young·Yi Lian·Taery Kim·Sehoon Ha
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Abstract

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.

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Courtney Young, Yi Lian, Taery Kim, Sehoon Ha. 2026-10-20. Navigation Explainability for Object Avoidance with LLMs for Assistive Robots. https://arxiv.org/abs/2610.28920

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