  For robots to be able to manipulate in unknown and unstructured environments
the robot should be capable of operating under partial observability of the
environment. Object occlusions and unmodeled environments are some of the
factors that result in partial observability. A common scenario where this is
encountered is manipulation in clutter. In the case that the robot needs to
locate an object of interest and manipulate it, it needs to perform a series of
decluttering actions to accurately detect the object of interest. To perform
such a series of actions, the robot also needs to account for the dynamics of
objects in the environment and how they react to contact. This is a non trivial
problem since one needs to reason not only about robot-object interactions but
also object-object interactions in the presence of contact. In the example
scenario of manipulation in clutter, the state vector would have to account for
the pose of the object of interest and the structure of the surrounding
environment. The process model would have to account for all the aforementioned
robot-object, object-object interactions. The complexity of the process model
grows exponentially as the number of objects in the scene increases. This is
commonly the case in unstructured environments. Hence it is not reasonable to
attempt to model all object-object and robot-object interactions explicitly.
Under this setting we propose a hypothesis based action selection algorithm
where we construct a hypothesis set of the possible poses of an object of
interest given the current evidence in the scene and select actions based on
our current set of hypothesis. This hypothesis set tends to represent the
belief about the structure of the environment and the number of poses the
object of interest can take. The agent's only stopping criterion is when the
uncertainty regarding the pose of the object is fully resolved.
