A robust grip is key to successful manipulation and joining of work pieces involved in any industrial assembly process. Stability of a grip depends on geometric and physical properties of the object as well as the gripper itself. Current state-of-the-art algorithms can usually predict if a grip would fail. However, they are not able to predict the force at which the gripped object starts to slip, which is critical as the object might be subjected to external forces, e.g. when joining it with another object. This research project aims to develop a AI-based approach for a grip metric based on tactile sensor data capturing the physical interactions between gripper and object. Thus, the maximum force that can be applied to the object before it begins to slip should be predicted before manipulating the object. The RGB image of the contact surface between the object and gripper jaws obtained from GelSight tactile sensors during the initial phase of the grip should serve as a training input for the grip metric. To generate such a data set, a pull experiment is designed using a UR 5 robot. Performing these experiments in real life to populate the data set is time consuming since different object classes, geometries, material properties and surface textures need to be considered to enhance the robustness of the prediction algorithm. Hence, a simulation model of the experimental setup has been developed to both speed up and automate the data generation process. In this paper, the design of this digital twin and the accuracy of the synthetic data are presented. State-of-the-art image comparison algorithms show that the simulated RGB images of the contact surface match the experimental data. In addition, the maximum pull forces can be reproduced for different object classes and grip scenarios. As a result, the synthetically generated data can be further used to train the neural grip metric network.

翻译：稳健的抓取是工业装配过程中工件成功操作与装配的关键。抓取稳定性取决于物体和夹爪本身的几何与物理特性。当前最先进的算法通常能够预测抓取是否失败，但无法预测被抓取物体开始滑移时的力——这一信息至关重要，因为物体可能承受外部力（例如与其他物体装配时）。本研究项目旨在开发一种基于触觉传感器数据的AI抓取度量方法，该数据捕捉夹爪与物体之间的物理交互。因此，应在操作物体前预测物体开始滑移前可施加的最大力。在抓取初始阶段，通过GelSight触觉传感器获得的物体与夹爪接触面的RGB图像将作为抓取度量的训练输入。为生成此类数据集，我们利用UR 5机器人设计了拉力实验。由于需考虑不同物体类别、几何形状、材料属性和表面纹理以增强预测算法的鲁棒性，在真实环境中执行这些实验耗时较长。因此，我们开发了实验装置的仿真模型，以加速并自动化数据生成过程。本文介绍了该数字孪生的设计及合成数据的准确性评估。最先进的图像比较算法表明，接触面的仿真RGB图像与实验数据匹配。此外，不同物体类别和抓取场景下的最大拉力可被复现。因此，合成生成的数据可进一步用于训练神经抓取度量网络。