The quality of expertise: implications of expert-novice differences for knowledge acquisition
ACM SIGART Bulletin - Special issue on knowledge acquisition
Mixed-Initiative Control for Remote Characterization of Hazardous Environments
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 5 - Volume 5
Comparing the usefulness of video and map information in navigation tasks
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Beyond usability evaluation: analysis of human-robot interaction at a major robotics competition
Human-Computer Interaction
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Final report for the DARPA/NSF interdisciplinary study on human-robot interaction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Shared understanding for collaborative control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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A dirty-bomb experiment conducted at the INL is used to evaluate the effectiveness and suitability of three different modes of robot control. The experiment uses three distinct user groups to understand how participants' background and training affect the way in which they use and benefit from autonomy. The results show that the target mode, which involves automated mapping and plume tracing together with a point and click tasking tool, provides the best performance for each group. This is true for objective performance such as source detection and localization accuracy as well as subjective measures such as perceived workload, frustration and preference. The best overall performance is achieved by the Explosive Ordinance Disposal group which has experience in both robot teleoperation and dirty bomb response. The user group that benefits least from autonomy is the Nuclear Engineers that have no experience with either robot operation or dirty bomb response. The group that benefits most from autonomy is the Weapons of Mass Destruction Civil Support Team that has extensive experience related to the task, but no robot training.