CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog

Fuente: arXiv
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Hauptverfasser: Yu, Cunjun, Wang, Zishuo, Xiao, Anxing, Li, Linfeng, Hsu, David
Format: Preprint
Veröffentlicht: 2026
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author Yu, Cunjun
Wang, Zishuo
Xiao, Anxing
Li, Linfeng
Hsu, David
author_facet Yu, Cunjun
Wang, Zishuo
Xiao, Anxing
Li, Linfeng
Hsu, David
contents Robot guide dogs offer navigation assistance that greatly expands the independent mobility of the visually impaired, but their effective use requires subtle human-robot coordination that is difficult for users to learn from generic verbal instructions. To tackle this challenge, we present CANINE, an automated coaching system that trains users for interactive navigation with a robot guide dog, through personalized, adaptive verbal feedback. CANINE decomposes a complex coordination task into sub-skills and operates at two levels. At the high level, it decides what to train by tracking the learner's proficiency across sub-skills using knowledge tracing and prioritizing training on the weakest areas. At the low level, CANINE decides how to train each sub-skill by observing each human practice episode, using foundation models to infer the underlying causes of errors, and generating targeted verbal corrections adaptively. A controlled study with blindfolded participants, treated as a proxy population for quantitative evaluation, demonstrates that CANINE significantly improves both learning efficiency and final navigation performance compared to generic verbal instructions. We further validate CANINE through a retention study and an exploratory case study. The retention study shows lasting skill improvement after two weeks. The case study confirms CANINE's effectiveness in training a visually impaired user, while revealing additional design considerations for real-world deployment. Both are well aligned with the findings of the controlled study. Project page: https://cunjunyu.github.io/project/canine/
format Preprint
id arxiv_https___arxiv_org_abs_2605_19501
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog
Yu, Cunjun
Wang, Zishuo
Xiao, Anxing
Li, Linfeng
Hsu, David
Robotics
Artificial Intelligence
Robot guide dogs offer navigation assistance that greatly expands the independent mobility of the visually impaired, but their effective use requires subtle human-robot coordination that is difficult for users to learn from generic verbal instructions. To tackle this challenge, we present CANINE, an automated coaching system that trains users for interactive navigation with a robot guide dog, through personalized, adaptive verbal feedback. CANINE decomposes a complex coordination task into sub-skills and operates at two levels. At the high level, it decides what to train by tracking the learner's proficiency across sub-skills using knowledge tracing and prioritizing training on the weakest areas. At the low level, CANINE decides how to train each sub-skill by observing each human practice episode, using foundation models to infer the underlying causes of errors, and generating targeted verbal corrections adaptively. A controlled study with blindfolded participants, treated as a proxy population for quantitative evaluation, demonstrates that CANINE significantly improves both learning efficiency and final navigation performance compared to generic verbal instructions. We further validate CANINE through a retention study and an exploratory case study. The retention study shows lasting skill improvement after two weeks. The case study confirms CANINE's effectiveness in training a visually impaired user, while revealing additional design considerations for real-world deployment. Both are well aligned with the findings of the controlled study. Project page: https://cunjunyu.github.io/project/canine/
title CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog
topic Robotics
Artificial Intelligence
url https://arxiv.org/abs/2605.19501