OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913435117158400 |
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| author | Meng, Li Qi, Zhao Shuchang, Lyu Chunlei, Wang Yujing, Ma Guangliang, Cheng Chenguang, Yang |
| author_facet | Meng, Li Qi, Zhao Shuchang, Lyu Chunlei, Wang Yujing, Ma Guangliang, Cheng Chenguang, Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13175 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping Meng, Li Qi, Zhao Shuchang, Lyu Chunlei, Wang Yujing, Ma Guangliang, Cheng Chenguang, Yang Robotics 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. |
| title | OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.13175 |