Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE

TL;DR

This paper introduces a hierarchical CVAE-based method for modeling and generating drawing and grinding trajectories that capture both local and global features, enabling high reproducibility and novel trajectory creation.

Contribution

It presents a novel hierarchical VAE framework that efficiently models complex trajectories with limited data and allows for the generation of new, unseen trajectories by model combination.

Findings

High reproducibility of generated trajectories

Effective generalization with limited training data

Ability to generate novel trajectories by model combination

Abstract

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.