Social Group Human-Robot Interaction: A Scoping Review of Computational Challenges

TL;DR

This scoping review analyzes computational challenges in group human-robot interaction, highlighting perception and behavior generation issues, and identifies research gaps to guide future advancements in socially interactive robots.

Contribution

The paper provides a comprehensive overview of recent computational challenges in group HRI and suggests directions for future research to address these issues.

Findings

Detection of groups, engagement, and conversation information are key perception challenges.

Developing approaching and conversational behaviors are main behavior generation challenges.

Identified gaps include subgroup detection and interpersonal relationship modeling.

Abstract

Group interactions are a natural part of our daily life, and as robots become more integrated into society, they must be able to socially interact with multiple people at the same time. However, group human-robot interaction (HRI) poses unique computational challenges often overlooked in the current HRI literature. We conducted a scoping review including 44 group HRI papers from the last decade (2015-2024). From these papers, we extracted variables related to perception and behaviour generation challenges, as well as factors related to the environment, group, and robot capabilities that influence these challenges. Our findings show that key computational challenges in perception included detection of groups, engagement, and conversation information, while challenges in behaviour generation involved developing approaching and conversational behaviours. We also identified research gaps, such as improving detection of subgroups and interpersonal relationships, and recommended future work in group HRI to help researchers address these computational challenges