A three-step model for the detection of stable grasp points with machine learning
Issue title: Selection of papers from the 21st EANN (Engineering Applications of Neural Networks) and 16th AIAI (Artificial Intelligence Applications and Innovations) Joint International Conference
Affiliations: Center for Applied Data Science (CfADS), Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Gütersloh, Germany
Correspondence:
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Corresponding author: Wolfram Schenck, Center for Applied Data Science (CfADS), Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Schulstraße 10, 33330 Gütersloh, Germany. E-mail: [email protected].
Abstract: 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.
Keywords: Robotic grasping, machine learning, deep learning, optimization, simulation, force-closure
