Human-Automation Collaboration

Faculty Members

Dr. Lydia Tapia

Graduate Students

Torin Adamson
Lewis Chiang
Yazied Hasan

Undergraduate Students

Valuable Sheffey
Kage Micaiah Weiss
Liz DiGioia


Dr. Patrick Kelley
Dr. Meeko Oishi
Daniel Gomez
Laura Patrizi

Related Projects

Moving Obstacle Avoidance

Automation systems that can find collision-free paths in uncertain environments with dynamic obstacles have highly significant applications, including aerial navigation, satellite coordination, and self-driving cars. However, many of these systems require human monitoring for special cases the automation can not handle. For these cases, an optimal experience results from effective collaboration between the human and automation. Our research focuses on finding ways to adapt technologies for successful human-automation collaboration.

For this purpose, we have developed Busy Beeway. Busy Beeway is a mobile game in which the player must guide the bee avatar, Beelinda, to a goal while avoiding stochastic obstacles (usually wasps). Depending on the mode, the player may complete a level solely or with varying levels of automation guidance. Automation methods are derived from our Moving Obstacle Avoidance Project.

Busy Beeway

Through user studies of Busy Beeway, we seek to answer questions such as:

  • How much is the human willing to rely on the automation?
  • What game states cause the human to take control?
  • What are effective mechanisms for conveying the guidance (including uncertainty) information to the player?



Publications & Papers

  • Torin Adamson, Hao-Tien Chiang, Meeko Oishi, Lydia Tapia. "Busy Beeway: A Game for Testing Human-Automation Collaboration for Navigation", In 2017 ACM SIGGRAPH Conference on Motion in Games (MIG), Barcelona, Spain, Nov. 2017. (pdf, Bibtex)