  Instance-aware segmentation of unseen objects is essential for a robotic
system in an unstructured environment. Although previous works achieved
encouraging results, they were limited to segmenting the only visible regions
of unseen objects. For robotic manipulation in a cluttered scene, amodal
perception is required to handle the occluded objects behind others. This paper
addresses Unseen Object Amodal Instance Segmentation (UOAIS) to detect 1)
visible masks, 2) amodal masks, and 3) occlusions on unseen object instances.
For this, we propose a Hierarchical Occlusion Modeling (HOM) scheme designed to
reason about the occlusion by assigning a hierarchy to a feature fusion and
prediction order. We evaluated our method on three benchmarks (tabletop,
indoors, and bin environments) and achieved state-of-the-art (SOTA)
performance. Robot demos for picking up occluded objects, codes, and datasets
are available at https://sites.google.com/view/uoais
