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Object placement is a fundamental task for robots, yet it remains challengingfor partially observed objects. Existing methods for object placement havelimitations, such as the requirement for a complete 3D model of the object orthe inability to handle complex shapes and novel objects that restrict theapplicability of robots in the real world. Herein, we focus on addressing theUnseen Object Placement (UOP}=) problem. We tackled the UOP problem using twomethods: (1) UOP-Sim, a large-scale dataset to accommodate various shapes andnovel objects, and (2) UOP-Net, a point cloud segmentation-based approach thatdirectly detects the most stable plane from partial point clouds. Our UOPapproach enables robots to place objects stably, even when the object's shapeand properties are not fully known, thus providing a promising solution forobject placement in various environments. We verify our approach throughsimulation and real-world robot experiments, demonstrating state-of-the-artperformance for placing single-view and partial objects. Robot demos, codes,and dataset are available at https://gistailab.github.io/uop/

Learning to Place Unseen Objects Stably using a Large-scale Simulation