Navigation beyond Wayfinding: Robots Collaborating with Visually Impaired Users for Environmental Interactions
This addresses a critical gap in assistive robotics for blind and visually impaired individuals by supporting real-world navigation tasks beyond simple obstacle avoidance, though it builds incrementally on existing robotic guidance systems.
The paper tackles the problem of enabling robots to assist visually impaired users not just in wayfinding but also in environmental interactions like pressing buttons or opening doors, by developing a collaborative system that alternates between leading and adapting modes, resulting in safer, smoother, and more efficient navigation compared to traditional methods, with performance improvements increasing for tasks requiring higher precision.
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.