Detect functions of objects based on operational task input

Yuchi Ishikawa, Haruya Ishikawa, Shuichi Akizuki, Yoshimitsu Aoki

研究成果: Article査読

抄録

Recently, the development of deep learning has enabled robots to grasp objects more reliably than ever. Given this fact, there is an increasing demand for helper robots or home robots. To make these robots real, robots need to understand not only how to grasp objects but also their functions. We propose a new representation for the functions of objects, task-oriented function, which is based on operational task input. This representation makes it possible to describe a variety of ways to use an object. We also propose a new dataset for task-oriented function and a network to detect it. This model reached 79.7% mean IOU in our dataset.

本文言語English
ページ(範囲)1136-1142
ページ数7
ジャーナルSeimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
85
12
DOI
出版ステータスPublished - 2019

ASJC Scopus subject areas

  • 機械工学

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