In this study, we propose a method to model the local and global features of the drawing/grinding trajectory with hierarchical Variational Autoencoders (VAEs). By combining two separately trained VAE models in a hierarchical structure, it is possible to generate trajectories with high reproducibility for both local and global features. The hierarchical generation network enables the generation of higher-order trajectories with a relatively small amount of training data. The simulation and experimental results demonstrate the generalization performance of the proposed method. In addition, we confirmed that it is possible to generate new trajectories, which have never been learned in the past, by changing the combination of the learned models.

翻译：在这项研究中,我们提出了一个方法,用等级变化式自动电算器(VAE)来模拟绘图/农业轨迹的当地和全球特点。通过将两个经过单独训练的VAE模型合并成一个等级结构,可以产生对当地和全球特征具有高度可复制性的轨迹。等级生成网络能够产生具有相对较少培训数据的较高级轨迹。模拟和实验结果显示了拟议方法的通用性。此外,我们确认,通过改变所学模型的组合,有可能产生过去从未学过的新轨迹。