Predicting the Intention to Interact with a Service Robot:the Role of
  Gaze Cues
Simone Arreghini Gabriele Abbate Alessandro Giusti Antonio Paolillo
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.