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

Task-assisted Motion Planning in Partially Observable Domains