An Estimation Framework for Passerby Engagement Interacting with Social Robots

TL;DR

This paper introduces a new framework for estimating user engagement with social robots, addressing multi-party interactions and real-world data challenges, with promising accuracy demonstrated through interaction duration analysis.

Contribution

It presents a novel engagement estimation method that considers multi-party behaviors and real-world interaction data, improving adaptive communication in social robots.

Findings

Interaction duration can be accurately estimated considering multi-party influences

The proposed model effectively captures engagement levels during robot interactions

Addressed key open problems in real-world social robot engagement estimation

Abstract

Social robots are expected to be a human labor support technology, and one application of them is an advertising medium in public spaces. When social robots provide information, such as recommended shops, adaptive communication according to the user's state is desired. User engagement, which is also defined as the level of interest in the robot, is likely to play an important role in adaptive communication. Therefore, in this paper, we propose a new framework to estimate user engagement. The proposed method focuses on four unsolved open problems: multi-party interactions, process of state change in engagement, difficulty in annotating engagement, and interaction dataset in the real world. The accuracy of the proposed method for estimating engagement was evaluated using interaction duration. The results show that the interaction duration can be accurately estimated by considering the influence of the behaviors of other people; this also implies that the proposed model accurately estimates the level of engagement during interaction with the robot.