  Task and motion planning represents a powerful set of hybrid planning methods
that combine reasoning over discrete task domains and continuous motion
generation. Traditional reasoning necessitates task domain models and enough
information to ground actions to motion planning queries. Gaps in this
knowledge often arise from sources like occlusion or imprecise modeling. This
work generates task and motion plans that include actions cannot be fully
grounded at planning time. During execution, such an action is handled by a
provided human-designed or learned closed-loop behavior. Execution combines
offline planned motions and online behaviors till reaching the task goal.
Failures of behaviors are fed back as constraints to find new plans. Forty
real-robot trials and motivating demonstrations are performed to evaluate the
proposed framework and compare against state-of-the-art. Results show faster
execution time, less number of actions, and more success in problems where
diverse gaps arise. The experiment data is shared for researchers to simulate
these settings. The work shows promise in expanding the applicable class of
realistic partially grounded problems that robots can address.
