An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks

by | Mar 1, 2020

Authors: Yuanzhi Cao, Xun Qian, Tianyi Wang, Rachel Lee, Ke Huo, Karthik Ramani
In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Machine tasks in workshops or factories are often a compound sequence of local, spatial, and body-coordinated human-machine interactions. Prior works have shown the merits of video-based and augmented reality (AR) tutoring systems for local tasks. However, due to the lack of a bodily representation of the tutor, they are not as effective for spatial and body-coordinated interactions. We propose avatars as an additional tutor representation to the existing AR instructions. In order to understand the design space of tutoring presence for machine tasks, we conduct a comparative study with 32 users. We aim to explore the strengths/limitations of the following four tutor options: video, non-avatar-AR, half-body+AR, and full-body+AR. The results show that users prefer the half-body+AR overall, especially for the spatial interactions. They have a preference for the full-body+AR for the body-coordinated interactions and the non-avatar-AR for the local interactions. We further discuss and summarize design recommendations and insights for future machine task tutoring systems.

Yuanzhi Cao

Yuanzhi Cao

As a senior Ph.D. student and a researcher in the Human-Computer Interaction (HCI) area, I specialize in designing interactive systems that provide novel Augmented Reality (AR) user experience for smartthing applications, such as Machines, Robots, and IoTs. [personal site]