Object placement is a fundamental task for robots, yet it remains challenging
for partially observed objects. Existing methods for object placement have
limitations, such as the requirement for a complete 3D model of the object or
the inability to handle complex shapes and novel objects that restrict the
applicability of robots in the real world. Herein, we focus on addressing the
Unseen Object Placement (UOP}=) problem. We tackled the UOP problem using two
methods: (1) UOP-Sim, a large-scale dataset to accommodate various shapes and
novel objects, and (2) UOP-Net, a point cloud segmentation-based approach that
directly detects the most stable plane from partial point clouds. Our UOP
approach enables robots to place objects stably, even when the object's shape
and properties are not fully known, thus providing a promising solution for
object placement in various environments. We verify our approach through
simulation and real-world robot experiments, demonstrating state-of-the-art
performance for placing single-view and partial objects. Robot demos, codes,
and dataset are available at https://gistailab.github.io/uop/Comment: 8 pages (main