An Interactive Augmented Reality Interface for Personalized Proxemics Modeling
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916349638344704 |
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| author | Nigro, Massimiliano O'Connell, Amy Groechel, Thomas Velentza, Anna-Maria Matarić, Maja |
| author_facet | Nigro, Massimiliano O'Connell, Amy Groechel, Thomas Velentza, Anna-Maria Matarić, Maja |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Interactive Augmented Reality Interface for Personalized Proxemics Modeling Nigro, Massimiliano O'Connell, Amy Groechel, Thomas Velentza, Anna-Maria Matarić, Maja Robotics 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. |
| title | An Interactive Augmented Reality Interface for Personalized Proxemics Modeling |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.03453 |