# A Variational Time Series Feature Extractor for Action Prediction

arXiv: https://arxiv.org/abs/1807.02350 · HTML: https://mlsanity.com/paper/1807.02350/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction](https://mlsanity.com/paper/2210.12418/) (2022)
- [Learning Multimodal Latent Dynamics for Human-Robot Interaction](https://mlsanity.com/paper/2311.16380/) (2023)

## Abstract (arXiv)

We propose a Variational Time Series Feature Extractor (VTSFE), inspired by the VAE-DMP model of Chen et al., to be used for action recognition and prediction. Our method is based on variational autoencoders. It improves VAE-DMP in that it has a better noise inference model, a simpler transition model constraining the acceleration in the trajectories of the latent space, and a tighter lower bound for the variational inference. We apply the method for classification and prediction of whole-body movements on a dataset with 7 tasks and 10 demonstrations per task, recorded with a wearable motion capture suit. The comparison with VAE and VAE-DMP suggests the better performance of our method for feature extraction. An open-source software implementation of each method with TensorFlow is also provided. In addition, a more detailed version of this work can be found in the indicated code repository. Although it was meant to, the VTSFE hasn't been tested for action prediction, due to a lack of time in the context of Maxime Chaveroche's Master thesis at INRIA.