Simone Arreghini, Gabriele Abbate, Alessandro Giusti, and Antonio Paolillo
Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano (Switzerland)
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.
The paper has been accepted at ICRA 2024. The pdf is available on arXiv here.
Cite this work:
@inproceedings{Arreghini:icra:2024,
author = {S. Arreghini and G. Abbate and A. Giusti and A. Paolillo},
title = {Predicting the Intention to Interact with a Service Robot: the Role of Gaze Cues},
booktitle={IEEE International Conference on Robotics and Automation},
pages = {--},
year = {2024},
}