We present an interface designed to operate social robots in a highly unpredictable context: a classroom, supporting user-centered design of innovative child–robot interactions. Having a functional prototype enables rich user data elicitation and analysis, essential for understanding user needs and deriving meaningful requirements. Deploying robots outside controlled laboratory conditions into a classroom, however, introduces challenges related to safety, robustness, and managing multiple, often noisy, interactions. Our system addresses these challenges by providing a safe, flexible and resilient interface that ensures safe operation while allowing improvisation and adaptability to unpredictable children's behaviors. The interface aims to be intuitive for non-expert users and support everyday teaching and learning activities in the classrooms. By prioritizing usability, modularity, and robustness, our approach facilitates iterative design, accelerates the transition from Wizard-of-Oz prototyping to autonomous behaviors, and contributes to making child–robot interaction technologies more accessible and practical for diverse application domains.

The detection of mutual gaze in the context of human-robot interaction is crucial for the understanding of human partners' behavior. Indeed, the monitoring of the users' gaze from a long distance enables the prediction of their intention and allows the robot to be proactive. Nonetheless, current implementations struggle or cannot operate in scenarios where detection from long distances is required. In this work, we propose a ROS2 software pipeline that detects mutual gaze up to 5 m of distance. The code relies on robust off-the-shelf perception algorithms.

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.

We consider a service robot that offers chocolate treats to people passing in its proximity: it has the capability of predicting in advance a person’s intention to interact, and to actuate an "offering" gesture, subtly extending the tray of chocolates towards a given target. We run the system for more than 5 hours across 3 days and two different crowded public locations; the system implements three possible behaviors that are randomly toggled every few minutes: passive (e.g. never performing the offering gesture); or active, triggered by either a naive distance-based rule, or a smart approach that relies on various behavioral cues of the user. We collect a real-world dataset that includes information on 1777 users with several spontaneous human-robot interactions and study the influence of robot actions on people’s behavior. Our comprehensive analysis suggests that users are more prone to engage with the robot when it proactively starts the interaction. We release the dataset and provide insights to make our work reproducible for the community. Also, we report qualitative observations collected during the acquisition campaign and identify future challenges and research directions in the domain of social human-robot interaction.