Adaptive learning of hand movement in human demonstration for robot action

Ngoc Hung Pham, Takashi Yoshimi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper describes a process for adaptive learning of hand movements in human demonstration for manipulation actions by robots using Dynamic Movement Primitives (DMPs) framework. The process includes 1) tracking hand movement from human demonstration, 2) segmenting hand movement, 3) adaptive learning with DMPs framework. We implement a extended DMPs model with a modified formulation for hand movement data observed from human demonstration including hand 3D position, orientation and fingers distance. We evaluate the generated movements by DMPs model which is reproduced without changes or adapted to change of goal of the movement. The adapted movement data is used to control a robot arm by spatial position and orientation of its end-effector with a parallel gripper.

Original languageEnglish
Pages (from-to)919-927
Number of pages9
JournalJournal of Robotics and Mechatronics
Issue number5
Publication statusPublished - 2017 Oct


  • Dynamic movement primitives
  • Hand movements
  • Learning from demonstration
  • Robot actions

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


Dive into the research topics of 'Adaptive learning of hand movement in human demonstration for robot action'. Together they form a unique fingerprint.

Cite this