用图神经网络集成提升复杂场景抓取精度，实测成功率91%。
Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Clutter
- 通过图神经网络集成，融合抓取点与上下文点进行几何推理。
- 在GraspNet-1Billion上平均精度提升35%，排名前三。
- 适合需要高精度、低延迟抓取的工业机器人应用。
在杂乱的真实环境中，由于传感器数据噪声大、不完整以及物体几何结构复杂，抓取位姿检测仍是重大挑战。本文提出Grasp the Graph 2.0（GtG 2.0）方法，一种轻量但高效的假设-验证机器人抓取框架，利用图神经网络集成从点云数据中进行高效几何推理。基于GtG 1.0的成功经验，该方法克服了先前对完整、无噪声点云及4自由度抓取的假设限制，采用常规抓取位姿生成器高效生成7自由度抓取候选。候选位姿由包含夹爪内部点和周围上下文点的图神经网络集成模型评估，显著提升了检测性能。在GraspNet-1Billion基准上，相较于同类方法，平均精度最高提升35%，排名位列前三。在3自由度Delta并联机械臂与Kinect-v1相机的实验中，抓取成功率达91%，杂乱环境完成率为100%，验证了其灵活性与可靠性。
原文摘要 · Abstract (English)
Grasp pose detection in cluttered, real-world environments remains a significant challenge due to noisy and incomplete sensory data combined with complex object geometries. This paper introduces Grasp the Graph 2.0 (GtG 2.0) method, a lightweight yet highly effective hypothesis-and-test robotics grasping framework which leverages an ensemble of Graph Neural Networks for efficient geometric reasoning from point cloud data. Building on the success of GtG 1.0, which demonstrated the potential of Graph Neural Networks for grasp detection but was limited by assumptions of complete, noise-free point clouds and 4-Dof grasping, GtG 2.0 employs a conventional Grasp Pose Generator to efficiently produce 7-Dof grasp candidates. Candidates are assessed with an ensemble Graph Neural Network model which includes points within the gripper jaws (inside points) and surrounding contextual points (outside points). This improved representation boosts grasp detection performance over previous methods using the same generator. GtG 2.0 shows up to a 35% improvement in Average Precision on the GraspNet-1Billion benchmark compared to hypothesis-and-test and Graph Neural Network-based methods, ranking it among the top three frameworks. Experiments with a 3-Dof Delta Parallel robot and Kinect-v1 camera show a success rate of 91% and a clutter completion rate of 100%, demonstrating its flexibility and reliability.
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