Region-aware Grasp Framework with Normalized Grasp Space for 6-DoF Grasping in Cluttered Scene

Regional geometric information is crucial for determining grasp poses. A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with multiple objects and potential collision, the definition of the grasp-relevant region remains inconsistent among methods, and the relationship between grasps and regional spaces remains incompletely investigated. In this paper, from a novel region-aware and grasp-centric viewpoint, we propose Normalized Grasp Space (NGS), unifying the grasp representation within a normalized regional space. The relationship among the grasp widths, region scales, and gripper sizes is considered and empowers our method to generalize to grippers and scenes with different scales. Leveraging the characteristics of the NGS, we find that 2D CNNs are surprisingly underestimated for complicated 6-DoF grasp detection tasks in clutter scenes and build a highly efficient Region-aware Normalized Grasp Network (RNGNet). Experiments conducted on the public benchmark show that our method achieves the best grasp detection results compared to the previous state-of-the-arts while attaining a real-time inference speed of approximately 50 FPS. Real-world cluttered scene clearance experiments underscore the effectiveness of our method with a higher success rate than other methods. Further human-to-robot handover and moving object grasping experiments demonstrate the potential of our proposed method for closed-loop grasping in dynamic scenarios.

Rethinking 6-Dof Grasp Detection: A Flexible Framework for High-Quality Grasping

Robotic grasping is a primitive skill for complex tasks and is fundamental to intelligence. For general 6-Dof grasping, most previous methods directly extract scene-level semantic or geometric information, while few of them consider the suitability for various downstream applications, such as target-oriented grasping. Addressing this issue, we rethink 6-Dof grasp detection from a grasp-centric view and propose a versatile grasp framework capable of handling both scene-level and target-oriented grasping. Our framework, FlexLoG, is composed of a Flexible Guidance Module and a Local Grasp Model. Specifically, the Flexible Guidance Module is compatible with both global (e.g., grasp heatmap) and local (e.g., visual grounding) guidance, enabling the generation of high-quality grasps across various tasks. The Local Grasp Model focuses on object-agnostic regional points and predicts grasps locally and intently. Experiment results reveal that our framework achieves over 18% and 23% improvement on unseen splits of the GraspNet-1Billion Dataset. Furthermore, real-world robotic tests in three distinct settings yield a 95% success rate.