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NavCog3 is a smartphone turn-by-turn navigation assistant system we developed specifically designed to enable independent navigation for people with visual impairments. Using off-the-shelf Bluetooth beacons installed in the surrounding environment and a commodity smartphone carried by the user, NavCog3 achieves unparalleled localization accuracy in real-world large-scale scenarios. By leveraging its accurate localization capabilities, NavCog3 guides the user through the environment and signals the presence of semantic features and points of interest in the vicinity (e.g., doorways, shops).To assess the capability of NavCog3 to promote independent mobility of individuals with visual impairments, we deployed and evaluated the system in two challenging real-world scenarios. The first scenario demonstrated the scalability of the system, which was permanently installed in a five-story shopping mall spanning three buildings and a public underground area. During the study, 10 participants traversed three fixed routes, and 43 participants traversed free-choice routes across the environment. The second scenario validated the system’s usability in the wild in a hotel complex temporarily equipped with NavCog3 during a conference for individuals with visual impairments. In the hotel, almost 14.2h of system usage data were collected from 37 unique users who performed 280 travels across the environment, for a total of 30,200m
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Personalized Dynamics Models for Adaptive Assistive Navigation Systems
Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination. Existing robotic and ubiquitous navigation technologies (e.g., portable, ground, or wearable systems) often operate in a generic, user-agnostic manner. However, to minimize confusion and navigation errors, our real-world analysis reveals a crucial need to adapt theinstructional guidance across different end-users with diverse mobility skills. To address this practical issue in scalable system design, we propose a novel model based reinforcement learning framework for personalizing the system-user interaction experience. When incrementally adapting the system to new users, we propose to use a weighted experts model for addressing data-efficiency limitations in transfer learning with deep models. A real-world dataset of navigation by blind users is used to show that the proposed approach allows for (1) more accurate long-term human behavior prediction (up to 20 seconds into the future) through improved reasoning over personal mobility characteristics, interaction with surrounding obstacles, and the current navigation goal, and (2) quick adaptation at the onset of learning, when data is limited.
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- Award ID(s):
- 1637927
- PAR ID:
- 10304266
- Date Published:
- Journal Name:
- 2nd Conference on Robot Learning
- Volume:
- 87
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
