Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909157589778432 |
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| author | Arreghini, Simone Abbate, Gabriele Giusti, Alessandro Paolillo, Antonio |
| author_facet | Arreghini, Simone Abbate, Gabriele Giusti, Alessandro Paolillo, Antonio |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_01986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues Arreghini, Simone Abbate, Gabriele Giusti, Alessandro Paolillo, Antonio Robotics Artificial Intelligence Machine Learning 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. |
| title | Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.01986 |