A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction

TL;DR

This study analyzes how a service robot's proactive behaviors influence human engagement in real-world settings, providing a dataset and insights for improving social human-robot interactions.

Contribution

The paper introduces a real-world dataset of human-robot interactions and compares passive and active robot behaviors, highlighting the impact of proactive actions on user engagement.

Findings

Proactive robot behaviors increase user engagement.

Users respond more positively when the robot initiates interaction.

The dataset enables future research in social human-robot interaction.

Abstract

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.