[1Q3-GS-11] Robot and real worlds: Multimodal information

[1Q3-GS-11-01] Category formation of real objects using Multimodal Variational Autoencoder

〇Yuto Yoshida¹, Akira Taniguchi¹, Kaede Hayashi¹, Tadahiro Taniguchi¹ (1. Univ. of Ritsumeikan)

Keywords:Deep generative model, Multimodal topic model, Variational autoencoder

We propose a neural network-based unsupervised object categorization method for a robot using multimodal sensor information.
The method is an extension of Multimodal Variational Autoencoder (MVAE). In the proposed method, Dirichlet prior is introduced for giving MVAE a clustering capability in the same way as Multimodal latent Dirichlet allocation (MLDA) that has been used for multimodal object categorization by a robot.
We performed comparative experiments with MLDA using both real objects and synthetic data.
The results show that our proposed model has a reduced computational costs compared to MLDA without deteriorating the classification accuracy.