Computer Science and Engineering, Department of


First Advisor

Justin Bradley

Second Advisor

Brittany Duncan

Third Advisor

Bonita Sharif

Date of this Version



A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Justin Bradley. Lincoln, Nebraska: May, 2020

Copyright 2020 Nancy Pham


Accounting for variance in human behavior is an integral part of interacting with robotic systems that share control between users and robots in order to reduce errors, improve performance, and maintain safety. In this work we focus on the shared control of a telepresence robot and how individual user traits may affect a person's performance while navigating the robot. This requires understanding which user qualities impact performance and cause conflicts -- with the ultimate goal of building shared controllers that adapt to those qualities. Toward this goal, we develop novel adaptive shared controllers and integrate the study of intrinsic user qualities alongside the study of these controllers, investigating how users react to different shared control paradigms. We implemented and analyzed two different types of shared controllers: 1) a switching controller that switches between a more relaxed and a more restrictive autonomy, utilizing repulsive potential fields, or “discouraging” methods that push users away from obstacles, and 2) an “Adapt” controller that “pulls” users toward a pre-computed optimal path, “encouraging” movement toward the goal. The Adapt controller utilizes a deliberative planner and accompanying novel “autopilot” mode to help users efficiently complete an obstacle course. We compare robot performance, user preference, and holistic user/robot performance of the shared controllers in the context of the user's intrinsic qualities. We find that there are significant differences in performance with users in different locus of control (LOC) groups, users with differing senses of presence, and users with varying immersive tendencies. Further, we found that our “encouraging” shared controller results in improved holistic user/robot performance compared to a “discouraging,” preventive controller. Based on our findings, we give a strong recommendation to use an adaptive shared controller, with varying degrees of control, depending on different user qualities. Further, we give specific guidelines on which types of adaptive shared controllers would be most compatible with the user qualities we studied in order to reduce conflicts, increase human-robot collaboration, and improve overall performance.

Adviser: Justin Bradley