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

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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]