Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2026

Abstract

Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization capabilities with the user’s ability to perform physical manipulation. The system alternates between two modes: lead mode, where the robot detects and guides the user to the target, and adaptation mode, where the robot adjusts its motion as the user interacts with the environment (e.g., opening a door). Evaluation results show that our system enables navigation that is safer, smoother, and more efficient than both a traditional white cane and a non-adaptive guiding system, with the performance gap widening as tasks demand higher precision in locating interaction targets. These findings highlight the promise of human–robot collaboration in advancing assistive technologies toward more generalizable and realistic navigation support.

Keywords

assistive technology, BVI, human-robot collaboration, navigation, visual impairment

Discipline

Databases and Information Systems | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

HRI '26: Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, March 16-19, Edinburgh

First Page

1201

Last Page

1210

ISBN

9798400721281

Identifier

10.1145/3757279.3785576

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3757279.3785576

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