Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://sites.google.com/view/graspgpt/.

翻译：任务导向抓取旨在预测能支持后续操作任务的物体抓取姿态。为建模物体、任务与抓取之间的复杂关系，现有方法将语义知识作为先验信息融入任务导向抓取流程。然而，现有语义知识通常基于封闭世界概念集构建，限制了其对预设集合外新概念的泛化能力。针对此问题，我们提出GraspGPT——一种基于大型语言模型的任务导向抓取框架，通过利用LLM的开放式语义知识，实现对新颖概念的零样本泛化。在Language Augmented TaskGrasp数据集上的实验表明，当泛化至训练集外的新概念时，GraspGPT在不同保留设置下均优于现有任务导向抓取方法。真实机器人实验进一步验证了GraspGPT的有效性。我们的代码、数据、附录及演示视频已公开于https://sites.google.com/view/graspgpt/。