OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping
<|reference_start|>OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping: 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.<|reference_end|>
@article{meng2024ovgnet:,
    title={OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary
  Robotic Grasping},
    author={Li Meng, Zhao Qi, Lyu Shuchang, Wang Chunlei, Ma Yujing, Cheng
  Guangliang, Yang Chenguang},
    journal={arXiv preprint arXiv:2407.13175},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13175},
    primaryClass={cs.RO}
}