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For robots to be able to manipulate in unknown and unstructured environmentsthe robot should be capable of operating under partial observability of theenvironment. Object occlusions and unmodeled environments are some of thefactors that result in partial observability. A common scenario where this isencountered is manipulation in clutter. In the case that the robot needs tolocate an object of interest and manipulate it, it needs to perform a series ofdecluttering actions to accurately detect the object of interest. To performsuch a series of actions, the robot also needs to account for the dynamics ofobjects in the environment and how they react to contact. This is a non trivialproblem since one needs to reason not only about robot-object interactions butalso object-object interactions in the presence of contact. In the examplescenario of manipulation in clutter, the state vector would have to account forthe pose of the object of interest and the structure of the surroundingenvironment. The process model would have to account for all the aforementionedrobot-object, object-object interactions. The complexity of the process modelgrows exponentially as the number of objects in the scene increases. This iscommonly the case in unstructured environments. Hence it is not reasonable toattempt to model all object-object and robot-object interactions explicitly.Under this setting we propose a hypothesis based action selection algorithmwhere we construct a hypothesis set of the possible poses of an object ofinterest given the current evidence in the scene and select actions based onour current set of hypothesis. This hypothesis set tends to represent thebelief about the structure of the environment and the number of poses theobject of interest can take. The agent's only stopping criterion is when theuncertainty regarding the pose of the object is fully resolved.

Policy Learning with Hypothesis based Local Action Selection