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

2026-05-19

AI Summary

This work addresses the challenge faced by visually impaired users in acquiring the fine-grained human-robot coordination skills necessary for effective navigation with robotic guide dogs through generic verbal instructions. To overcome this, the paper introduces CANINE, a novel system that uniquely integrates knowledge tracing with foundation models to deliver personalized, causally driven adaptive training. At the high level, knowledge tracing identifies users’ weak sub-skills and prioritizes targeted interventions; at the low level, foundation models analyze operational errors and generate precise, contextual feedback. Experimental results demonstrate that CANINE significantly enhances both learning efficiency and navigation performance, with benefits sustained over a two-week period. The system’s efficacy is further validated through real-world case studies involving actual visually impaired users.

Abstract

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/

Problem

• robot guide dog
• visually impaired
• interactive navigation
• human-robot coordination
• user training

Innovation

• adaptive coaching
• robot guide dog
• interactive navigation
• foundation models
• knowledge tracing