Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues
<|reference_start|>Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues: For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person's gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84.5% to 91.2%); the distance at which an accurate classification can be achieved improves from 2.4 m to 3.2 m. We also quantify the system's ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.<|reference_end|>
@article{arreghini2024predicting,
    title={Predicting the Intention to Interact with a Service Robot:the Role of
  Gaze Cues},
    author={Simone Arreghini, Gabriele Abbate, Alessandro Giusti, Antonio Paolillo},
    journal={arXiv preprint arXiv:2404.01986},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01986},
    primaryClass={cs.RO cs.AI cs.LG}
}