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Recent work in robotic manipulation focuses on object retrieval in clutteredspaces under occlusion. Nevertheless, the majority of efforts lack an analysisof conditions for the completeness of the approaches or the methods apply onlywhen objects can be removed from the workspace. This work formulates thegeneral, occlusion-aware manipulation task, and focuses on safe objectreconstruction in a confined space with in-place rearrangement. It proposes aframework that ensures safety with completeness guarantees. Furthermore, analgorithm, which is an instantiation of this abstract framework for monotoneinstances is developed and evaluated empirically by comparing against a randomand a greedy baseline on randomly generated experiments in simulation. Even forcluttered scenes with realistic objects, the proposed algorithm significantlyoutperforms the baselines and maintains a high success rate across experimentalconditions.

Safe, Occlusion-Aware Manipulation for Online Object Reconstruction in Confined Spaces