An Interactive Augmented Reality Interface for Personalized Proxemics Modeling
<|reference_start|>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 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.<|reference_end|>
@article{nigro2024an,
    title={An Interactive Augmented Reality Interface for Personalized Proxemics
  Modeling},
    author={Massimiliano Nigro, Amy O'Connell, Thomas Groechel, Anna-Maria
  Velentza, Maja Matari'c},
    journal={arXiv preprint arXiv:2408.03453},
    year={2024},
    doi={10.1109/MRA.2024.3415108},
    archivePrefix={arXiv},
    eprint={2408.03453},
    primaryClass={cs.RO}
}