6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling
<|reference_start|>6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling: 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.<|reference_end|>
@article{wang20246-dof,
    title={6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and
  Graspable Balance Sampling},
    author={Hanwen Wang, Ying Zhang, Yunlong Wang, Jian Li},
    journal={arXiv preprint arXiv:2407.01209},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01209},
    primaryClass={cs.RO}
}