  Retrieving objects from clutters is a complex task, which requires multiple
interactions with the environment until the target object can be extracted.
These interactions involve executing action primitives like grasping or pushing
as well as setting priorities for the objects to manipulate and the actions to
execute. Mechanical Search (MS) is a framework for object retrieval, which uses
a heuristic algorithm for pushing and rule-based algorithms for high-level
planning. While rule-based policies profit from human intuition in how they
work, they usually perform sub-optimally in many cases. Deep reinforcement
learning (RL) has shown great performance in complex tasks such as taking
decisions through evaluating pixels, which makes it suitable for training
policies in the context of object-retrieval. In this work, we first formulate
the MS problem in a principled formulation as a hierarchical POMDP. Based on
this formulation, we propose a hierarchical policy learning approach for the MS
problem. For demonstration, we present two main parameterized sub-policies: a
push policy and an action selection policy. When integrated into the
hierarchical POMDP's policy, our proposed sub-policies increase the success
rate of retrieving the target object from less than 32% to nearly 80%, while
reducing the computation time for push actions from multiple seconds to less
than 10 milliseconds.
