Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping
<|reference_start|>Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping: A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsistent, and the relationship between grasps and regional spaces remains incompletely investigated. In this paper, we propose Normalized Grasp Space (NGS) from a novel region-aware viewpoint, unifying the grasp representation within a normalized regional space and benefiting the generalizability of methods. Leveraging the NGS, we find that CNNs are underestimated for 3D feature extraction and 6-DoF grasp detection in clutter scenes and build a highly efficient Region-aware Normalized Grasp Network (RNGNet). Experiments on the public benchmark show that our method achieves significant >20% performance gains while attaining a real-time inference speed of approximately 50 FPS. Real-world cluttered scene clearance experiments underscore the effectiveness of our method. Further, human-to-robot handover and dynamic object grasping experiments demonstrate the potential of our proposed method for closed-loop grasping in dynamic scenarios.<|reference_end|>
@article{chen2024region-aware,
    title={Region-aware Grasp Framework with Normalized Grasp Space for Efficient
  6-DoF Grasping},
    author={Siang Chen, Pengwei Xie, Wei Tang, Dingchang Hu, Yixiang Dai, Guijin
  Wang},
    journal={arXiv preprint arXiv:2406.01767},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01767},
    primaryClass={cs.RO}
}