Abstract
Task-oriented grasping (TOG) is more challenging than simple object grasping because it requires precise identification of object parts and careful selection of grasping areas to ensure effective and robust manipulation. While recent approaches have trained large-scale vision-language models to integrate part-level object segmentation with task-aware grasp planning, their instability in part recognition and grasp inference limits their ability to generalize across diverse objects and tasks. To address this issue, we introduce a novel, geometry-centric strategy for more generalizable TOG that does not rely on semantic features from visual recognition, effectively overcoming the viewpoint sensitivity of model-based approaches. Our main proposals include: 1) an object-part-task ontology for functional part selection based on intuitive human commands, constructed using a Large Language Model (LLM); 2) a sampling-based geometric analysis method for identifying the selected object part from observed point clouds, incorporating multiple point distribution and distance metrics; and 3) a similarity matching framework for imitative grasp planning, utilizing similar known objects with pre-existing segmentation and grasping knowledge as references to guide the planning for unknown targets. We validate the high accuracy of our approach in functional part selection, identification, and grasp generation through real-world experiments. Additionally, we demonstrate the method's generalization capabilities to novel-category objects by extending existing ontological knowledge, showcasing its adaptability to a broad range of objects and tasks.
Abstract (translated)
任务导向抓取（TOG）相较于简单物体抓取更具挑战性，因为它需要精准识别物体部件并审慎选择抓取区域，以确保操作的有效性与鲁棒性。尽管近期方法已通过训练大规模视觉-语言模型来整合部件级物体分割与任务感知抓取规划，但其在部件识别与抓取推理方面的不稳定性，限制了跨多样化物体与任务的泛化能力。针对此问题，我们提出一种新颖的、以几何为中心的策略，以实现更具泛化能力的TOG，该方法不依赖视觉识别中的语义特征，从而有效克服了基于模型方法对视角敏感的缺陷。我们的核心提案包括：1）基于直观人类指令的功能部件选择物体-部件-任务本体论，通过大语言模型（LLM）构建；2）一种基于采样的几何分析方法，用于从观测点云中识别所选物体部件，整合多种点分布与距离度量；3）一种用于模仿式抓取规划的相似性匹配框架，利用具有预存分割与抓取知识的相似已知物体作为参考，引导未知目标的规划。我们通过真实实验验证了该方法在功能部件选择、识别与抓取生成方面的高精度。此外，通过扩展现有本体论知识，我们展示了该方法对新颖类别物体的泛化能力，凸显其适应广泛物体与任务的灵活性。
URL
https://arxiv.org/abs/2603.26412
