OVGNet：用于开放词汇机器人抓取的统一视觉-语言框架
OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping
AI总结：
本文提出OVGNet，一个集成开放词汇学习的统一视觉-语言框架，并构建大规模基准数据集，通过两个对齐模块增强感知，使机器人能抓取新物体，实验平均准确率达71.2%（基础）和64.4%（新类别）。
摘要：
识别并抓取新类别物体在真实世界机器人应用中仍然是一个关键且具有挑战性的问题。尽管其重要性显著，但针对这一特定领域的研究仍然有限。为解决这一问题，我们提出了一种新颖的框架，将开放词汇学习无缝集成到机器人抓取领域，使机器人具备熟练处理新物体的能力。我们的贡献有三方面。首先，我们提出了一个大规模基准数据集，专门用于评估开放词汇抓取任务的性能。其次，我们提出了一个统一的视觉-语言框架，作为机器人在成功抓取基础物体和新物体时的指导。第三，我们引入了两个对齐模块，旨在增强机器人抓取过程中的视觉-语言感知。大量实验验证了我们方法的有效性和实用性。值得注意的是，我们的框架在我们的新数据集上对基础类别和新类别的平均准确率分别达到了71.2%和64.4%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
