Modern manufacturing processes are in a state of flux, as they adapt to increasing demand for flexible and self-configuring production. This poses challenges for training workers to rapidly master new machine operations and processes, i.e. machine tasks. Conventional in-person training is effective but requires time and effort of experts for each worker trained and not scalable. Recorded tutorials, such as video-based or augmented reality (AR), permit more efficient scaling. However, unlike in-person tutoring, existing recorded tutorials lack the ability to adapt to workers’ diverse experiences and learning behaviors. We present AdapTutAR, an adaptive task tutoring system that enables experts to record machine task tutorials via embodied demonstration and train learners with different AR tutoring contents adapting to each user’s characteristics. The adaptation is achieved by continually monitoring learners’ tutorial-following status and adjusting the tutoring content on-the-fly and in-situ. The results of our user study evaluation have demonstrated that our adaptive system is more effective and preferable than the non-adaptive one.
AdapTutAR: An Adaptive Tutoring System for Machine Tasks in Augmented Reality
Authors: Gaoping Huang*, Xun Qian*, Tianyi Wang, Fagun Patel, Maitreya Sreeram, Yuanzhi Cao, Karthik Ramani, and Alexander J. Quinn
In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Xun Qian is a Ph.D. student in the School of Mechanical Engineering at Purdue University since Fall 2018. Before joining the C Design Lab, he received his Master's degree in Mechanical Engineering at Cornell University, and Bachelor's degree in Mechanical Engineering at University of Science and Technology Beijing. His current research interests lie in development of novel human-computer interactions leveraging AR/VR/MR, Deep Learning, and Cloud Computing. For more details, please visit his personal website at xun-qian.com