Tatsuro Yamada — accepted AI-conference papers

2018

Paired Recurrent Autoencoders for Bidirectional Translation Between Robot Actions and Linguistic Descriptions

Tatsuro Yamada, Hiroyuki Matsunaga, Tetsuya Ogata

RA-L 2018

We propose a novel deep learning framework for bidirectional translation between robot actions and their linguistic descriptions. Our model consists of two recurrent autoencoders (RAEs). One RAE learns to encode action sequences as fixed-dimensional vectors in a way that allows the sequences to be r

Cited by 68 Source ↗Scholar ↗

2015

Attractor representations of language-behavior structure in a recurrent neural network for human-robot interaction

Tatsuro Yamada, Shingo Murata, Hiroaki Arie, Tetsuya Ogata

IROS 2015poster

In recent years there has been increased interest in studies that explore integrative learning of language and other modalities by using neural network models. However, for practical application to human-robot interaction, the acquired semantic structure between language and meaning has to be availa…

Cited by 6 Source ↗Scholar ↗