  Recognizing and grasping novel-category objects remains a crucial yet
challenging problem in real-world robotic applications. Despite its
significance, limited research has been conducted in this specific domain. To
address this, we seamlessly propose a novel framework that integrates
open-vocabulary learning into the domain of robotic grasping, empowering robots
with the capability to adeptly handle novel objects. Our contributions are
threefold. Firstly, we present a large-scale benchmark dataset specifically
tailored for evaluating the performance of open-vocabulary grasping tasks.
Secondly, we propose a unified visual-linguistic framework that serves as a
guide for robots in successfully grasping both base and novel objects. Thirdly,
we introduce two alignment modules designed to enhance visual-linguistic
perception in the robotic grasping process. Extensive experiments validate the
efficacy and utility of our approach. Notably, our framework achieves an
average accuracy of 71.2\% and 64.4\% on base and novel categories in our new
dataset, respectively.
