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

arXiv: https://arxiv.org/abs/2605.19501 · HTML: https://mlsanity.com/paper/2605.19501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 81 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 30 of 36)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models](https://mlsanity.com/paper/2502.19417/) (2025)
- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- ["No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy](https://mlsanity.com/paper/2301.02555/) (2023)
- [Understanding Large-Language Model (LLM)-powered Human-Robot Interaction](https://mlsanity.com/paper/2401.03217/) (2024)
- [GPTCoach: Towards LLM-Based Physical Activity Coaching](https://mlsanity.com/paper/2405.06061/) (2024)
- [Multi-Armed Bandits for Intelligent Tutoring Systems](https://mlsanity.com/paper/1310.3174/) (2013)
- [LaMI: Large Language Models for Multi-Modal Human-Robot Interaction](https://mlsanity.com/paper/2401.15174/) (2024)
- [Learning Dynamic Robot-to-Human Object Handover from Human Feedback](https://mlsanity.com/paper/1603.06390/) (2016)
- [Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation](https://mlsanity.com/paper/1604.02336/) (2016)
- [Robotic Guide Dog: Leading a Human with Leash-Guided Hybrid Physical Interaction](https://mlsanity.com/paper/2103.14300/) (2021)
- [Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics](https://mlsanity.com/paper/1412.5967/) (2014)
- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [Assistive Teaching of Motor Control Tasks to Humans](https://mlsanity.com/paper/2211.14003/) (2022)
- [Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach](https://mlsanity.com/paper/2203.03927/) (2022)
- [COACH: Cooperative Robot Teaching](https://mlsanity.com/paper/2302.06199/) (2023)
- [FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization](https://mlsanity.com/paper/2506.14968/) (2025)
- [Computational Teaching for Driving via Multi-Task Imitation Learning](https://mlsanity.com/paper/2410.01608/) (2024)
- [Generating Language Corrections for Teaching Physical Control Tasks](https://mlsanity.com/paper/2306.07012/) (2023)
- [TeachingBot: Robot Teacher for Human Handwriting](https://mlsanity.com/paper/2309.11848/) (2023)
- [Shared Autonomy for Proximal Teaching](https://mlsanity.com/paper/2502.19899/) (2025)
- [Beyond Omakase: Designing Shared Control for Navigation Robots with Blind People](https://mlsanity.com/paper/2503.21997/) (2025)

## Built on by (most cited first, 1 of 1)

- [RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision](https://mlsanity.com/paper/2609.20330/) (2026)

## Abstract (arXiv)

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/