This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups. Our approach is agnostic to the design of the gripper and does not require gripper-specific training data. In particular, we propose a two-step approach, where first, a neural network predicts pixel-wise grasp quality for an input image to indicate areas that are generally graspable. Second, an optimization step determines the optimal gripper selection and corresponding grasp poses based on configured gripper layouts and activation schemes. In addition, we introduce a method for automated labeling for supervised training of the grasp quality network. Experimental evaluations on a real-world industrial application with bin picking scenes of varying difficulty demonstrate the effectiveness of our method.

翻译：本文提出了一种新颖的、适用于多吸盘夹爪的无模型抓取姿态预测方法。该方法与夹爪的具体设计无关，且无需针对特定夹爪的训练数据。具体而言，我们提出了一种两阶段方法：首先，一个神经网络根据输入图像预测逐像素的抓取质量，以指示大致可抓取的区域；其次，通过一个优化步骤，根据配置的夹爪布局和激活方案，确定最优夹爪选择及相应的抓取姿态。此外，我们还引入了一种用于抓取质量网络监督训练的自动标注方法。在具有不同难度的料箱拣选场景的实际工业应用中的实验评估，证明了我们方法的有效性。