# Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE

arXiv: https://arxiv.org/abs/2111.10954 · HTML: https://mlsanity.com/paper/2111.10954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Hierarchical Imitation and Reinforcement Learning](https://mlsanity.com/paper/1803.00590/) (2018)
- [Neural Task Programming: Learning to Generalize Across Hierarchical Tasks](https://mlsanity.com/paper/1710.01813/) (2017)
- [Anomaly Detection With Conditional Variational Autoencoders](https://mlsanity.com/paper/2010.05531/) (2020)
- [Drawing and Recognizing Chinese Characters with Recurrent Neural Network](https://mlsanity.com/paper/1606.06539/) (2016)
- [Hierarchical Behavioral Repertoires with Unsupervised Descriptors](https://mlsanity.com/paper/1804.07127/) (2018)
- [Interpretable Latent Spaces for Learning from Demonstration](https://mlsanity.com/paper/1807.06583/) (2018)

## Built on by (most cited first, 1 of 1)

- [Mamba as a motion encoder for robotic imitation learning](https://mlsanity.com/paper/2409.02636/) (2024)

## Abstract (arXiv)

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.