  Supervisory-based human-robot teams are deployed in various dynamic and
extreme environments (e.g., space exploration). Achieving high task performance
in such environments is critical, as a mistake may lead to significant monetary
loss or human injury. Task performance may be augmented by adapting the
supervisory interface's interactions or autonomy levels based on the human
supervisor's workload level, as workload is related to task performance.
Typical adaptive systems rely solely on the human's overall or cognitive
workload state to select what adaptation strategy to implement; however,
overall workload encompasses many dimensions (i.e., cognitive, physical,
visual, auditory, and speech) called workload components. Selecting an
appropriate adaptation strategy based on a complete human workload state
(rather than a single workload dimension) may allow for more impactful
adaptations that ensure high task performance. A Supervisory-Based Adaptive
Human-Robot Teaming Architecture (SAHRTA) that selects an appropriate level of
autonomy or system interaction based on a complete real-time multi-dimensional
workload estimate and predicted future task performance is introduced. SAHRTA
was shown to improve overall task performance in a physically expanded version
of the NASA Multi-Attribute Task Battery.
