  Recent work in robotic manipulation focuses on object retrieval in cluttered
spaces under occlusion. Nevertheless, the majority of efforts lack an analysis
of conditions for the completeness of the approaches or the methods apply only
when objects can be removed from the workspace. This work formulates the
general, occlusion-aware manipulation task, and focuses on safe object
reconstruction in a confined space with in-place rearrangement. It proposes a
framework that ensures safety with completeness guarantees. Furthermore, an
algorithm, which is an instantiation of this abstract framework for monotone
instances is developed and evaluated empirically by comparing against a random
and a greedy baseline on randomly generated experiments in simulation. Even for
cluttered scenes with realistic objects, the proposed algorithm significantly
outperforms the baselines and maintains a high success rate across experimental
conditions.
