# OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping

arXiv: https://arxiv.org/abs/2407.13175 · HTML: https://mlsanity.com/paper/2407.13175/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping under Flexible Language Instructions](https://mlsanity.com/paper/2503.16013/) (2025)
- [RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation](https://mlsanity.com/paper/2603.14880/) (2026)
- [OpenTie: Open-vocabulary Sequential Rebar Tying System](https://mlsanity.com/paper/2509.00064/) (2025)
- [Learning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments](https://mlsanity.com/paper/2603.02511/) (2026)

## Abstract (arXiv)

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.