Article

An Explainability Framework for LLM-Based Assistive Robot Obstacle Avoidance Navigation

The study proposes the Context-Aware Navigation Explanation framework, which uses a large language model combined with perceptual context and planned trajectories to generate natural-language explanations for assistive robot obstacle avoidance and navigation. The system is deployed on a Unitree Go2 platform in indoor navigation scenarios to evaluate how different explanation context configurations affect user trust and explanation satisfaction. Results show a strong positive correlation between explanation satisfaction and trust, and that the configuration combining obstacle information with future trajectory produced the best explanations.

Part of the event: Proposing an explainability framework for assistive robot navigation

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