Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction
<|reference_start|>Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction: Social robots often rely on visual perception to understand their users and the environment. Recent advancements in data-driven approaches for computer vision have demonstrated great potentials for applying deep-learning models to enhance a social robot's visual perception. However, the high computational demands of deep-learning methods, as opposed to the more resource-efficient shallow-learning models, bring up important questions regarding their effects on real-world interaction and user experience. It is unclear how will the objective interaction performance and subjective user experience be influenced when a social robot adopts a deep-learning based visual perception model. We employed state-of-the-art human perception and tracking models to improve the visual perception function of the Pepper robot and conducted a controlled lab study and an in-the-wild human-robot interaction study to evaluate this novel perception function for following a specific user with other people present in the scene.<|reference_end|>
@article{zhong2024improving,
    title={Improving Visual Perception of a Social Robot for Controlled and
  In-the-wild Human-robot Interaction},
    author={Wangjie Zhong, Leimin Tian, Duy Tho Le, Hamid Rezatofighi},
    journal={arXiv preprint arXiv:2403.01766},
    year={2024},
    doi={10.1145/3610978.3640648},
    archivePrefix={arXiv},
    eprint={2403.01766},
    primaryClass={cs.RO cs.CV}
}