Impact of Users’ Locus of Control on Presence and Performance in Telepresence Robot Operation

This study investigated the impact of user qualities on presence and performance with the goal of aiding in the development of shared autonomy algorithms that adapt to users based on inferred qualities. Previous works in shared autonomy have focused on adapting to a single user in a single interaction but have neglected sensing and adapting in real-time to personal qualities such as locus of control (LOC). This study collected user commands, performance, and personal data of 60 participants in a telepresence robot driving task to understand their relationships and generate a strategy for shared autonomy systems which adapt to individual users. Based on LOC, as assessed by a survey, users were divided into three groups, ``High Internal\u27\u27 who believe they have control over their own fate, ``High External\u27\u27 who attribute the occurrence of events to external factors, and ``Average\u27\u27 users who fall between ``High Internal\u27\u27 and ``High External\u27\u27. The time taken by ``High External\u27\u27 users was 33.89% more and the distance travelled by these users was 27.62% more than that of the ``Average\u27\u27 users in restrictive mode. In relaxed mode, presence perceived by ``High Internal\u27\u27 users was 4.79% more than that of the ``Average\u27\u27 users. It was also found that the ``High Internal\u27\u27 users issued 29.12% more commands, had 69.17% more conflicting commands, 33.2% more percentage of command conflicts, took 41.18% longer duration, and travelled 17.74% farther in restrictive mode than in relaxed mode. These results indicate that the users with different LOC performed differently in different modes of shared control and the users seeking more control fought against autonomy in restrictive mode but performed better in relaxed mode. These findings suggest that user qualities can be inferred from a brief set of interactions and autonomy can potentially be adapted during runtime to improve user performance. This work is meaningful to the human-robot interaction community because it broadens previous findings in the community, and to the communities affiliated with robotics and autonomous systems as a whole in order to better adapt to the novice users of the future. Adviser: Brittany A. Dunca