An Interactive Augmented Reality Interface for Personalized Proxemics Modeling

Understanding and respecting personal space preferences is essential for socially assistive robots designed for older adult users. This work introduces and evaluates a novel personalized context-aware method for modeling users' proxemics preferences during human-robot interactions. Using an int...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Nigro, Massimiliano, O'Connell, Amy, Groechel, Thomas, Anna-Maria Velentza, Matarić, Maja
Format: Article
Language:English
Subjects:
Augmented reality
Customization
Deep learning
Man-machine interfaces
Modelling
Robots
Service robots
Virtual reality
Online Access:Get full text
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Summary:Understanding and respecting personal space preferences is essential for socially assistive robots designed for older adult users. This work introduces and evaluates a novel personalized context-aware method for modeling users' proxemics preferences during human-robot interactions. Using an interactive augmented reality interface, we collected a set of user-preferred distances from the robot and employed an active transfer learning approach to fine-tune a specialized deep learning model. We evaluated this approach through two user studies: 1) a convenience population study (N = 24) to validate the efficacy of the active transfer learning approach; and 2) a user study involving older adults (N = 15) to assess the system's usability. We compared the data collected with the augmented reality interface and with the physical robot to examine the relationship between proxemics preferences for a virtual robot versus a physically embodied robot. We found that fine-tuning significantly improved model performance: on average, the error in testing decreased by 26.97% after fine-tuning. The system was well-received by older adult participants, who provided valuable feedback and suggestions for future work.
ISSN:2331-8422
DOI:10.48550/arxiv.2408.03453