Robotic Tools and Immersive Media for Scalable Learning

Abstract:
Dynamic visual media, such as videos, are an effective and scalable vehicle for learning, especially for topics involving hands-on skills and real-world environments (e.g. fabrication). However, traditional video content is limited by its nature to show specific perspectives of a task's full 3D environment, and it is difficult to capture complex spatial tasks from fixed cameras.
This talk will present the speaker's PhD research, in which he has been investigating how robotic systems and immersive media can help visual-media-based learning go beyond the limitations of traditional cameras and screens. He will introduce three specific approaches he has taken: 1) Utilizing camera robots to autonomously capture regions of interest; 2) Allowing both viewers' and video creators' real-time control over the robotic cameras' positioning; 3) Enabling immersive capture and viewing of the spatial environment, through 360 cameras and virtual reality playback. Furthermore, he will reflect on the findings and discuss his future research plans.

Biodata:
Jiannan Li is a PhD candidate in Computer Science at the University of Toronto, where he is advised by Prof. Ravin Balakrishnan and Prof. Tovi Grossman. His research interest lies at the intersection of Human-Computer Interaction, Human-Robot Interaction, and Virtual Reality, with a focus on building robotic and immersive systems for improving visual-media-based learning and communication. He has also worked on a range of other novel interactive technologies, including wearables, drones, and transparent displays. He has published at top-tier HCI venues, such as ACM CHI, UIST, and CSCW, and received a CHI best paper honourable mention award.