Human-robot collaboration presents an opportunity
to improve the efficiency of manufacturing and assembly processes,
particularly for aerospace manufacturing where tight
integration and variability in the build process make physical
isolation of robotic-only work challenging. In this paper, we
develop a robotic scheduling and control capability that adapts
to the changing preferences of a human co-worker or supervisor
while providing strong guarantees for synchronization and timing
of activities. This innovation is realized through dynamic execution
of a flexible optimal scheduling policy that accommodates
temporal disturbance. We describe the Adaptive Preferences
Algorithm that computes the flexible scheduling policy and
show empirically that execution is fast, robust, and adaptable
to changing preferences for workflow. We achieve satisfactory
computation times, on the order of seconds for moderatelysized
problems, and demonstrate the capability for human-robot
teaming using a small industrial robot.