A Decade of Human-Robot Interaction Through Immersive Lenses: Reviewing Extended Reality as a Research Instrument in Social Robotics

TL;DR

This paper reviews a decade of research using Extended Reality in social robotics, highlighting current practices, limitations, and proposing a roadmap to enhance XR as a reliable research tool in human-robot interaction studies.

Contribution

It provides a systematic review of XR in social robotics, identifies key gaps and challenges, and proposes a four-phase roadmap to advance XR as a research instrument.

Findings

Most research is lab-based with limited hardware reporting

Robots are mainly passive visual stimuli with untapped biosignal data

Research samples are homogeneous, predominantly Western, young, and male

Abstract

Over the past decade, Extended Reality (XR), including Virtual, Augmented, and Mixed Reality, gained attention as a research instrument in human-robot interaction studies, but remains underexplored in empirical investigations of social robotics. To map the field, we systematically reviewed empirical studies from 2015 to 2025. Of 6,527 peer-reviewed articles, only 33 met strict inclusion criteria. We examined (1) how XR and virtual social robots are used, focusing on the software and hardware employed and the application contexts in which they are deployed, (2) data collection and analysis methods, (3) demographics of the researchers and participants, and (4) the challenges and future directions. Our findings show that social XR-HRI research is still driven by laboratory simulations, while crucial specifications - such as the hardware, software, and robots used - are often not reported. Robots typically act as passive and hardly interactive visual stimulus, while the rich biosignal (e.g., eye-tracking) and logging (e.g. motion capturing) functions of modern head-mounted displays remain largely untapped. While there are gaps in demographic reporting, the research teams and samples are predominantly tech-centric, Western, young, and male. Key limitations include hardware delays, small homogeneous samples, and short study cycles. We propose a four-phase roadmap to establish social XR-HRI as a reliable research medium, which includes (1) strengthen application contexts, (2) more robust and testable technological iterations, (3) embedding diversity in samples and research teams, and (4) the need for reporting standards, e.g., in form of a suitable taxonomy. Advancing in these directions is essential for XR to mature from a lab prototype into an ecologically valid research instrument for social robotics.