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

• URL: http://arxiv.org/abs/2605.19501v1
• Date: Tue, 19 May 2026 07:52:32 GMT
• Title: CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog
• Abstract summary: CANINE is an automated coaching system that trains users for interactive navigation with a robot guide dog.<n>It decomposes a complex coordination task into sub-skills and operates at two levels.<n>A controlled study shows CANINE significantly improves both learning efficiency and final navigation performance.
• Score: 21.615817274710597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/

Related papers

• Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization [11.210709234919237]
  We introduce SENSEI, a framework that infers user misconceptions from interaction behavior.<n>Our approach departs from action- or trajectory-level interventions by operating over a structured knowledge representation.<n>A user study shows that our method identifies real human misconceptions and provides effective guidance.
  arXiv  Detail & Related papers  (2026-06-04T02:25:19Z)
• From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication [6.5337939793709445]
  A robotic guide dog is an assistive quadruped robot that helps visually impaired people in obstacle avoidance and navigation.<n>We develop a novel dialog system for robotic guide dogs that uses LLMs to verbalize both navigational plans and scenes.
  arXiv  Detail & Related papers  (2026-03-13T02:11:00Z)
• GuideNav: User-Informed Development of a Vision-Only Robotic Navigation Assistant For Blind Travelers [14.840633179067295]
  GuideNav is a vision-only, teach-and-repeat navigation system.<n>Inspired by how guide dogs are trained and assist their handlers, GuideNav autonomously repeats a path demonstrated by a sighted person using a robot.<n>In field tests, GuideNav consistently achieved kilometer-scale route following across five outdoor environments.
  arXiv  Detail & Related papers  (2025-12-05T20:57:48Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy [70.45420918526926]
  We present LILAC, a framework for incorporating and adapting to natural language corrections online during execution. Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot. We show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users.
  arXiv  Detail & Related papers  (2023-01-06T15:03:27Z)
• System Configuration and Navigation of a Guide Dog Robot: Toward Animal Guide Dog-Level Guiding Work [8.775408795666477]
  A robot guide dog has compelling advantages over animal guide dogs for cost-effectiveness, potential for mass production, and low maintenance burden. Previous studies were conducted with little or no consideration of how the guide dog handler and the guide dog work as a team for navigation. We propose a semantic-aware local path planner that enables safe and efficient guiding work by utilizing semantic information about the environment.
  arXiv  Detail & Related papers  (2022-10-24T16:11:20Z)
• Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment [52.07473934146584]
  We guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications. It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level.
  arXiv  Detail & Related papers  (2022-08-04T23:53:51Z)
• Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play [18.88705140683795]
  Task-oriented dialog systems are often trained on human/human dialogs, such as collected from Wizard-of-Oz interfaces. This paper investigates two approaches to training agent-bots and user-bots through self-play, in which they autonomously explore an API environment.
  arXiv  Detail & Related papers  (2021-09-20T14:52:25Z)
• Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation [145.84123197129298]
  Language instruction plays an essential role in the natural language grounded navigation tasks. We exploit to train a more robust navigator which is capable of dynamically extracting crucial factors from the long instruction. Specifically, we propose a Dynamic Reinforced Instruction Attacker (DR-Attacker), which learns to mislead the navigator to move to the wrong target.
  arXiv  Detail & Related papers  (2021-07-23T14:11:31Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)
• Assisted Perception: Optimizing Observations to Communicate State [112.40598205054994]
  We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairments. We synthesize new observations that lead to more accurate internal state estimates when processed by the user.
  arXiv  Detail & Related papers  (2020-08-06T19:08:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.