Graspness Discovery in Clutters for Fast and Accurate Grasp Detection

• URL: http://arxiv.org/abs/2406.11142v1
• Date: Mon, 17 Jun 2024 02:06:47 GMT
• Title: Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
• Authors: Chenxi Wang, Hao-Shu Fang, Minghao Gou, Hongjie Fang, Jin Gao, Cewu Lu,
• Abstract summary: "graspness" is a quality based on geometry cues that distinguishes graspable areas in cluttered scenes. We develop a neural network named cascaded graspness model to approximate the searching process. Experiments on a large-scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin.
• Score: 57.81325062171676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt uniform sampling to select grasp candidates. However, we discover that ignoring where to grasp greatly harms the speed and accuracy of current grasp pose detection methods. In this paper, we propose "graspness", a quality based on geometry cues that distinguishes graspable areas in cluttered scenes. A look-ahead searching method is proposed for measuring the graspness and statistical results justify the rationality of our method. To quickly detect graspness in practice, we develop a neural network named cascaded graspness model to approximate the searching process. Extensive experiments verify the stability, generality and effectiveness of our graspness model, allowing it to be used as a plug-and-play module for different methods. A large improvement in accuracy is witnessed for various previous methods after equipping our graspness model. Moreover, we develop GSNet, an end-to-end network that incorporates our graspness model for early filtering of low-quality predictions. Experiments on a large-scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin (30+ AP) and achieves a high inference speed. The library of GSNet has been integrated into AnyGrasp, which is at https://github.com/graspnet/anygrasp_sdk.

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