Robotic grasping in dynamic environments is still one of the main challenges in automation tasks. Advances in deep learning methods and computational power suggest that the problem of robotic grasping can be solved by using a huge amount of training data and deep networks. Despite these huge accomplishments, the acceptance and usage in real-world scenarios is still limited. This is mainly due to the fact that the collection of the training data is expensive, and that the trained network is a black box. While the collection of the training data can sometimes be facilitated by carrying it out in simulation, the trained networks, however, remain a black box. In this study, a three-step model is presented that profits both from the advantages of using a simulation approach and deep neural networks to identify and evaluate grasp points. In addition, it even offers an explanation for failed grasp attempts. The first step is to find all grasp points where the gripper can be lowered onto the table without colliding with the object. The second step is to determine, for the grasp points and gripper parameters from the first step, how the object moves while the gripper is closed. Finally, in the third step, for all grasp points from the second step, it is predicted whether the object slips out of the gripper during lifting. By this simplification, it is possible to understand for each grasp point why it is stable and – just as important – why others are unstable or not feasible. All of the models employed in each of the three steps and the resulting Overall Model are evaluated. The predicted grasp points from the Overall Model are compared to the grasp points determined analytically by a force-closure algorithm, to validate the stability of the predicted grasps.

中文翻译：

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用机器学习检测稳定抓握点的三步模型

动态环境中的机器人抓取仍然是自动化任务的主要挑战之一。深度学习方法和计算能力的进步表明，可以通过使用大量训练数据和深度网络来解决机器人抓取问题。尽管取得了这些巨大的成就，但在现实世界场景中的接受和使用仍然有限。这主要是因为训练数据的收集成本很高，而且训练后的网络是一个黑匣子。虽然有时可以通过在模拟中执行来促进训练数据的收集，但是经过训练的网络仍然是一个黑匣子。在这项研究中，提出了一个三步模型，该模型受益于使用模拟方法和深度神经网络来识别和评估抓点的优势。此外，它甚至为失败的抓取尝试提供了解释。第一步是找到所有抓持点，在这些抓持点上，夹具可以降低到桌子上而不会与物体发生碰撞。第二步是针对第一步的抓取点和抓手参数确定抓手关闭时物体如何移动。最后，在第三步中，对于第二步的所有抓取点，预测物体在提升过程中是否从抓手中滑出。通过这种简化，可以理解每个把握点为什么它是稳定的，以及——同样重要的是——为什么其他人不稳定或不可行。在三个步骤中的每一个步骤中使用的所有模型以及由此产生的总体模型都被评估。