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arXiv · 2407.01209

6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling

Hanwen Wang·Ying Zhang·Yunlong Wang·Jian Li
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Abstract

6-DoF grasp detection of small-scale grasps is crucial for robots to perform specific tasks. This paper focuses on enhancing the recognition capability of small-scale grasping, aiming to improve the overall accuracy of grasping prediction results and the generalization ability of the network. We propose an enhanced receptive field method that includes a multi-radii cylinder grouping module and a passive attention module. This method enhances the receptive field area within the graspable space and strengthens the learning of graspable features. Additionally, we design a graspable balance sampling module based on a segmentation network, which enables the network to focus on features of small objects, thereby improving the recognition capability of small-scale grasping. Our network achieves state-of-the-art performance on the GraspNet-1Billion dataset, with an overall improvement of approximately 10% in average precision@k (AP). Furthermore, we deployed our grasp detection model in pybullet grasping platform, which validates the effectiveness of our method.

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Hanwen Wang, Ying Zhang, Yunlong Wang, Jian Li. 2024-07-01. 6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling. https://arxiv.org/abs/2407.01209

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