An Interactive Augmented Reality Interface for Personalized Proxemics Modeling

TL;DR

This paper presents a novel augmented reality interface combined with active transfer learning to personalize proxemics modeling for socially assistive robots, improving accuracy and user acceptance among older adults.

Contribution

It introduces an interactive AR-based method for collecting user preferences and fine-tuning deep models for personalized proxemics, a novel approach in human-robot interaction.

Findings

Fine-tuning reduced model error by 26.97%.

Older adults found the system usable and engaging.

AR interface effectively collected proxemics preferences.

Abstract

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.