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/.

翻译：我们研究了在多样化的支撑物上悬挂各种抓取物体的课题。悬挂物体是日常生活多个方面都会遇到的普遍任务。然而，物体和支撑物在形状和结构上可能存在显著差异，这带来了两个挑战性问题：(1) 确定不同物体与支撑物之间任务相关的几何结构，以及 (2) 识别出能够适应支撑物形状变化的鲁棒动作序列。为此，我们提出了语义关键点轨迹（SKT）——一种与物体无关且高度通用、适用于各类日常物体的表征方法。我们还提出了形状条件轨迹形变网络（SCTDN），该模型通过学习基于支撑物任务相关几何结构特征对模板轨迹进行形变，从而生成SKT。我们开展了大量实验，结果表明，我们的框架在成功率和推理时间上显著优于现有的机器人悬挂方法。最后，我们仅在仿真环境中训练的框架在真实世界中展现出了有前景的悬挂效果。视频和补充材料请访问我们的项目网页：https://hcis-lab.github.io/SKT-Hang/。