Tool-Use Model Considering Tool Selection by a Robot Using Deep Learning

Namiko Saito, Kitae Kim, Shingo Murata, Tetsuya Ogata, Shigeki Sugano

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We propose a tool-use model that can select tools that require neither labeling nor modeling of the environment and actions. With this model, a robot can choose a tool by itself and perform the operation that matches a human command and the environmental situation. To realize this, we use deep learning to train sensory motor data recorded during tool selection and tool use as experienced by a robot. The experience includes two types of selection, namely according to function and according to size, thereby allowing the robot to handle both situations. For evaluation, the robot is required to generate motion either in an untrained situation or using an untrained tool. We confirm that the robot can choose and use a tool that is suitable for achieving the target task.

本文言語English
ホスト出版物のタイトル2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018
出版社IEEE Computer Society
ページ814-819
ページ数6
ISBN(電子版)9781538672839
DOI
出版ステータスPublished - 2019 1 23
イベント18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018 - Beijing, China
継続期間: 2018 11 62018 11 9

出版物シリーズ

名前IEEE-RAS International Conference on Humanoid Robots
2018-November
ISSN(印刷版)2164-0572
ISSN(電子版)2164-0580

Conference

Conference18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018
CountryChina
CityBeijing
Period18/11/618/11/9

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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