  We study the problem of hanging a wide range of grasped objects on diverse
supporting items. Hanging objects is a ubiquitous task that is encountered in
numerous aspects of our everyday lives. However, both the objects and
supporting items can exhibit substantial variations in their shapes and
structures, bringing two challenging issues: (1) determining the task-relevant
geometric structures across different objects and supporting items, and (2)
identifying a robust action sequence to accommodate the shape variations of
supporting items. To this end, we propose Semantic Keypoint Trajectory (SKT),
an object-agnostic representation that is highly versatile and applicable to
various everyday objects. We also propose Shape-conditioned Trajectory
Deformation Network (SCTDN), a model that learns to generate SKT by deforming a
template trajectory based on the task-relevant geometric structure features of
the supporting items. We conduct extensive experiments and demonstrate
substantial improvements in our framework over existing robot hanging methods
in the success rate and inference time. Finally, our simulation-trained
framework shows promising hanging results in the real world. For videos and
supplementary materials, please visit our project webpage:
https://hcis-lab.github.io/SKT-Hang/.
