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.

翻译：在杂乱的真实世界环境中，由于噪声和残缺的感知数据与复杂物体几何形状的叠加，抓取姿态检测仍然是一项重大挑战。本文提出Grasp the Graph 2.0（GtG 2.0）方法——一种轻量级且高效的假设-测试机器人抓取框架，该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。在GtG 1.0成功的基础上（该版本展示了图神经网络在抓取检测中的潜力，但受限于完整无噪声点云和4自由度抓取的假设），GtG 2.0采用传统抓取姿态生成器高效产生7自由度抓取候选。候选姿态通过集成图神经网络模型进行评估，该模型包含夹爪内部点及周围上下文点。这种改进的表征方式在使用相同生成器的前提下，较先前方法提升了抓取检测性能。在GraspNet-10亿基准测试中，GtG 2.0相比假设-测试及基于图神经网络的方法，平均精度最高提升35%，使其跻身前三强框架之列。通过3自由度Delta并联机器人与Kinect-v1相机的实验表明，该方法成功率达91%，杂乱场景完成率为100%，充分证明了其灵活性与可靠性。