  We present an integrated Task-Motion Planning framework for robot navigation
in belief space. Autonomous robots operating in real world complex scenarios
require planning in the discrete (task) space and the continuous (motion)
space. To this end, we propose a framework for integrating belief space
reasoning within a hybrid task planner. The expressive power of PDDL+ combined
with heuristic-driven semantic attachments performs the propagated and
posterior belief estimates while planning. The underlying methodology for the
development of the combined hybrid planner is discussed, providing suggestions
for improvements and future work. Furthermore we validate key aspects of our
approach using a realistic scenario in simulation.
