This paper presents an expert-guided Mixed-Initiative (MI) variable-autonomy controller for remotely operated mobile robots. The controller enables switching between different Level(s) of Autonomy (LOA) during task execution initiated by either the human operator or/andthe robot. The controller is evaluated in two Search and Rescue (SAR) inspired experiments, one with a simulated robot and test arena  and one with a real robot in a realistic environment. Our hypothesis is that timely switching between different LOAs will enable the system to overcome various performance degrading factors and thus enable superior performance compared to systems in which LOAs cannot switch on-the-fly. Statistically validated analyses from the two experiments provide evidence that: a) Human-Initiative (HI) systems outperform purely teleoperated or autonomous systems in navigation tasks; b) MI systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload  . Results also reinforce previous Human-Robot interaction (HRI) evidence regarding the importance of the operator's personality traits and their trust in the autonomous system. Lastly, the paper provides empirical evidence that identify two major challenges for MI control: a) the design of context-aware MI controllers ; and b) the conflict for control between the robot  and the operator  .
This paper presents an Expert-guided Mixed-Initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two disaster response inspired experiments, one with a simulated robot and test arena , and one with a real robot in a realistic environment. Analyses from the two experiments provide evidence that: a) Human-Initiative (HI) systems outperform systems with single modes of operation, such as pure teleoperation, in navigation tasks; b) in the context of the simulated robot experiment, Mixed-Initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI . Results also reinforce previous human-robot interaction evidence regarding the importance of the operator's personality traits and their trust in the autonomous system. Lastly, our experiment on a physical robot provides empirical evidence that identify two major challenges for MI control: a) the design of context-aware MI control systems ; and b) the conflict for control between the robot 's MI control system and the operator . Insights regarding these challenges are discussed and ways to tackle them are proposed .
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expert-guided
Expert-guided
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(MI) variable-autonomy controller
Control Switcher (EMICS)
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controller
EMICS
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Level(s) of Autonomy (LOA)
levels of autonomy
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or/andthe robot. The controller
and/or the EMICS. The EMICS
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Search and Rescue (SAR)
disaster response
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,
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Our hypothesis is that timely switching between different LOAs will enable the system to overcome various performance degrading factors and thus enable superior performance compared to systems in which LOAs cannot switch on-the-fly. Statistically validated analyses
Analyses
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purely teleoperated or autonomous systems
systems with single modes of operation, such as pure teleoperation,
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MI
in the context of the simulated robot experiment, Mixed-Initiative (MI)
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compared to HI
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Human-Robot interaction (HRI)
human-robot interaction
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the paper
our experiment on a physical robot
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controllers
control systems
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's MI control system
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. Insights regarding these challenges are discussed and ways to tackle them are proposed