An approach to learn hand movements for robot actions from human demonstrations

P. N. Hung, Takashi Yoshimi

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

We present an approach to learn and generate movements for robot actions from human demonstrations using Dynamical Movement Primitives (DMPs) framework. The human hand movements are recorded by a motion tracker using a Kinect sensor with a color-marker glove. We segment an observed movement into simple motion units which are called as motion primitives. Then, each motion primitive will be encoded by DMPs models. These DMPs models are used to generate a desired movement by from learning a sample movement with the ability of generalization and adaption to new situation as the change of a desired goal. We extend standard DMPs for multi-dimensional data including the hand 3D position as control signal for movement trajectory, the hand orientation representation as control signal for robot end-effector orientation, and the distance between two fingers as control signal for opening/closing state of a robot gripper.

本文言語English
ホスト出版物のタイトルSII 2016 - 2016 IEEE/SICE International Symposium on System Integration
出版社Institute of Electrical and Electronics Engineers Inc.
ページ711-716
ページ数6
ISBN(電子版)9781509033294
DOI
出版ステータスPublished - 2017 2 6
イベント2016 IEEE/SICE International Symposium on System Integration, SII 2016 - Sapporo, Japan
継続期間: 2016 12 132016 12 15

出版物シリーズ

名前SII 2016 - 2016 IEEE/SICE International Symposium on System Integration

Other

Other2016 IEEE/SICE International Symposium on System Integration, SII 2016
国/地域Japan
CitySapporo
Period16/12/1316/12/15

ASJC Scopus subject areas

  • 生体医工学
  • 制御およびシステム工学
  • 機械工学
  • 人工知能
  • ハードウェアとアーキテクチャ
  • 制御と最適化

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