Audio-Visual Hybrid Approach for Filling Mass Estimation

Reina Ishikawa, Yuichi Nagao, Ryo Hachiuma, Hideo Saito

研究成果: Conference contribution

抄録

Object handover is a fundamental and essential capability for robots interacting with humans in many applications such as household chores. In this challenge, we estimate the physical properties of a variety of containers with different fillings such as container capacity and the type and percentage of the content to achieve collaborative physical handover between humans and robots. We introduce multi-modal prediction models using audio-visual-datasets of people interacting with containers distributed by CORSMAL.

本文言語English
ホスト出版物のタイトルPattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
編集者Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
出版社Springer Science and Business Media Deutschland GmbH
ページ437-450
ページ数14
ISBN(印刷版)9783030687922
DOI
出版ステータスPublished - 2021
イベント25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Milan, Italy
継続期間: 2021 1 102021 1 11

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12668 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference25th International Conference on Pattern Recognition Workshops, ICPR 2020
国/地域Italy
CityMilan
Period21/1/1021/1/11

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

フィンガープリント

「Audio-Visual Hybrid Approach for Filling Mass Estimation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル