Real-time liquid pouring motion generation: End-to-end sensorimotor coordination for unknown liquid dynamics trained with deep neural networks

Namiko Saito, Nguyen Ba Dai, Tetsuya Ogata, Hiroki Mori, Shigeki Sugano

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a sensorimotor dynamical system model for pouring unknown liquids. With our system, a robot holds and shakes a bottle to estimate the characteristics of the contained liquid, such as viscosity and fill level, without calculating to determine their parameters. Next, the robot pours a specified amount of the liquid into another container. The system needs to integrate information on the robot's actions, the liquids, the container, and the surrounding environment to perform the estimation and execute a continuous pouring motion using the same model. We use deep neural networks (DNN) to construct the system. The DNN model repeats prediction and execution of the actions to be taken in the next time step based on the input sensorimotor data, including camera images, force sensor data, and joint angles. At the same time, the DNN model acquires liquid characteristics in the internal state. We confirmed that the DNN model can control the robot to pour a desired amount of liquid with unknown viscosity and fill level.

本文言語English
ホスト出版物のタイトルIEEE International Conference on Robotics and Biomimetics, ROBIO 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1077-1082
ページ数6
ISBN(電子版)9781728163215
DOI
出版ステータスPublished - 2019 12
イベント2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 - Dali, China
継続期間: 2019 12 62019 12 8

出版物シリーズ

名前IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

Conference

Conference2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
国/地域China
CityDali
Period19/12/619/12/8

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • 機械工学
  • 制御と最適化

フィンガープリント

「Real-time liquid pouring motion generation: End-to-end sensorimotor coordination for unknown liquid dynamics trained with deep neural networks」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