2024/06/03 by Siang Chen, Pengwei Xie, Chen, Siang +7 · 3 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Robot Manipulation and Learning #Robotics (cs.RO) #Robotics and Automated Systems
paper · pdf · doi:10.48550/arxiv.2406.01767
openalex publication_date 2024/06/03 · openalex created_date 2024/06/08 · openalex updated_date 2026/07/28
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.