An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks (CHI2020)
Yuanzhi Cao, Xun Qian, Tianyi Wang, Rachel Lee, Ke Huo, and Karthik Ramani. "An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks." In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI2020)
Abstract: 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.