  In this paper we address mobile manipulation planning problems in the
presence of sensing and environmental uncertainty. In particular, we consider
mobile sensing manipulators operating in environments with unknown geometry and
uncertain movable objects, while being responsible for accomplishing tasks
requiring grasping and releasing objects in a logical fashion. Existing
algorithms either do not scale well or neglect sensing and/or environmental
uncertainty. To face these challenges, we propose a hybrid control
architecture, where a symbolic controller generates high-level manipulation
commands (e.g., grasp an object) based on environmental feedback, an
informative planner designs paths to actively decrease the uncertainty of
objects of interest, and a continuous reactive controller tracks the sparse
waypoints comprising the informative paths while avoiding a priori unknown
obstacles. The overall architecture can handle environmental and sensing
uncertainty online, as the robot explores its workspace. Using numerical
simulations, we show that the proposed architecture can handle tasks of
increased complexity while responding to unanticipated adverse configurations.
