Xun Qian

Xun Qian

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
CAPturAR: An Augmented Reality Tool for Authoring Human-Involved Context-Aware Applications

CAPturAR: An Augmented Reality Tool for Authoring Human-Involved Context-Aware Applications

Tianyi Wang*, Xun Qian*, Fengming He, Xiyun Hu, Ke Huo, Yuanzhi Cao, Karthik Ramani
In Proceedings of the 2020 UIST 33rd ACM User Interface Software and Technology Symposium

Recognition of human behavior plays an important role in context-aware applications. However, it is still a challenge for end-users to build personalized applications that accurately recognize their own activities. Therefore, we present CAPturAR,...

Vipo: Spatial-Visual Programming with Functions for Robot-IoT Workflows

Vipo: Spatial-Visual Programming with Functions for Robot-IoT Workflows

Gaoping Huang, Pawan S. Rao, Meng-Han Wu, Xun Qian, Shimon Y. Nof, Karthik Ramani, and Alexander J. Quinn
In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Mobile robots and IoT (Internet of Things) devices can increase productivity, but only if they can be programmed by workers who understand the domain. This is especially true in manufacturing. Visual programming in the spatial context of the...

An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks

An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks

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....

GhostAR: A Time-space Editor for Embodied Authoring of Human-Robot Collaborative Task with Augmented Reality

GhostAR: A Time-space Editor for Embodied Authoring of Human-Robot Collaborative Task with Augmented Reality

Yuanzhi Cao*, Tianyi Wang*, Xun Qian, Pawan S. Rao, Manav Wadhawan, Ke Huo, Karthik Ramani
Proceedings of the 32nd Annual Symposium on User Interface Software and Technology. ACM, 2019.

We present GhostAR, a time-space editor for authoring and acting Human-Robot-Collaborative (HRC) tasks in-situ. Our system adopts an embodied authoring approach in Augmented Reality (AR), for spatially editing the actions and programming the robots...