Grounded language understanding for manipulation instructions using GAN-based classification

Komei Sugiura, Hisashi Kawai

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

The target task of this study is grounded language understanding for domestic service robots (DSRs). In particular, we focus on instruction understanding for short sentences where verbs are missing. This task is of critical importance to build communicative DSRs because manipulation is essential for DSRs. Existing instruction understanding methods usually estimate missing information only from non-grounded knowledge; therefore, whether the predicted action is physically executable or not was unclear. In this paper, we present a grounded instruction understanding method to estimate appropriate objects given an instruction and situation. We extend the Generative Adversarial Nets (GAN) and build a GAN-based classifier using latent representations. To quantitatively evaluate the proposed method, we have developed a data set based on the standard data set used for visual question answering (VQA). Experimental results have shown that the proposed method gives the better result than baseline methods.

本文言語English
ホスト出版物のタイトル2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ519-524
ページ数6
ISBN(電子版)9781509047888
DOI
出版ステータスPublished - 2018 1 24
外部発表はい
イベント2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Okinawa, Japan
継続期間: 2017 12 162017 12 20

出版物シリーズ

名前2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
2018-January

Conference

Conference2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017
国/地域Japan
CityOkinawa
Period17/12/1617/12/20

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用

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