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arXiv · 2404.01986

Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues

Simone Arreghini·Gabriele Abbate·Alessandro Giusti·Antonio Paolillo
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Abstract

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.

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Simone Arreghini, Gabriele Abbate, Alessandro Giusti, Antonio Paolillo. 2024-04-02. Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues. https://arxiv.org/abs/2404.01986

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