English

Transferring Studies Across Embodiments: A Case Study in Confusion Detection

Human-Computer Interaction 2022-06-06 v1 Artificial Intelligence Computers and Society Machine Learning Robotics

Abstract

Human-robot studies are expensive to conduct and difficult to control, and as such researchers sometimes turn to human-avatar interaction in the hope of faster and cheaper data collection that can be transferred to the robot domain. In terms of our work, we are particularly interested in the challenge of detecting and modelling user confusion in interaction, and as part of this research programme, we conducted situated dialogue studies to investigate users' reactions in confusing scenarios that we give in both physical and virtual environments. In this paper, we present a combined review of these studies and the results that we observed across these two embodiments. For the physical embodiment, we used a Pepper Robot, while for the virtual modality, we used a 3D avatar. Our study shows that despite attitudinal differences and technical control limitations, there were a number of similarities detected in user behaviour and self-reporting results across embodiment options. This work suggests that, while avatar interaction is no true substitute for robot interaction studies, sufficient care in study design may allow well executed human-avatar studies to supplement more challenging human-robot studies.

Keywords

Cite

@article{arxiv.2206.01493,
  title  = {Transferring Studies Across Embodiments: A Case Study in Confusion Detection},
  author = {Na Li and Robert Ross},
  journal= {arXiv preprint arXiv:2206.01493},
  year   = {2022}
}

Comments

6 pages paper for the 1st Workshop on the representation, sharing and evaluation of multimodal agent interaction, link: https://cltl.github.io/mmai2022/

R2 v1 2026-06-24T11:38:07.603Z