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

• URL: http://arxiv.org/abs/2404.01986v1
• Date: Tue, 2 Apr 2024 14:22:54 GMT
• Title: Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues
• Authors: Simone Arreghini, Gabriele Abbate, Alessandro Giusti, Antonio Paolillo,
• Abstract summary: Service robots need to perceive as early as possible that an approaching person intends to interact. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact. Our main contribution is a study of the benefit of features representing the person's gaze in this context.
• Score: 51.58558750517068
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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