Task-oriented grasping of unfamiliar objects is a necessary skill for robots
in dynamic in-home environments. Inspired by the human capability to grasp such
objects through intuition about their shape and structure, we present a novel
zero-shot task-oriented grasping method leveraging a geometric decomposition of
the target object into simple, convex shapes that we represent in a graph
structure, including geometric attributes and spatial relationships. Our
approach employs minimal essential information - the object's name and the
intended task - to facilitate zero-shot task-oriented grasping. We utilize the
commonsense reasoning capabilities of large language models to dynamically
assign semantic meaning to each decomposed part and subsequently reason over
the utility of each part for the intended task. Through extensive experiments
on a real-world robotics platform, we demonstrate that our grasping approach's
decomposition and reasoning pipeline is capable of selecting the correct part
in 92% of the cases and successfully grasping the object in 82% of the tasks we
evaluate. Additional videos, experiments, code, and data are available on our
project website: this https URL

通过几何分解目标物体为简单的凸形状并将其表示为图形结构，包括几何属性和空间关系，利用大型语言模型的常识推理能力来动态分配语义含义，并对每个分解部分在特定任务中的效用进行推理，实现了一种新颖的零样本任务导向抓取方法。通过在真实世界的机器人平台上进行广泛实验，我们证明了我们的抓取方法的分解与推理管道在 92% 的情况下能够选择正确的部分，并且在 82% 的任务中成功抓取目标物体。