Motion imitation learning and real-time movement generation of humanoid using evolutionary algorithm

Ga lam Park, Syung kwon Ra, Chang hwan Kim, Jae bok Song

Research output: Contribution to journalArticle

Abstract

This paper presents a framework to generate human-like movements of a humanoid in real time using the movement primitive database of a human. The framework consists of two processes: 1) the offline motion imitation learning based on an Evolutionary Algorithm and 2) the online motion generation of a humanoid using the database updated by the motion imitation learning. For the offline process, the initial database contains the kinetic characteristics of a human, since it is full of human's captured motions. The database then develops through the proposed framework of motion learning based on an Evolutionary Algorithm, having the kinetic characteristics of a humanoid in aspect of minimal torque or joint jerk. The humanoid generates human-like movements for a given purpose in real time by linearly interpolating the primitive motions in the developed database. The movement of catching a ball was examined in simulation.

Original languageEnglish
Pages (from-to)1038-1046
Number of pages9
JournalJournal of Institute of Control, Robotics and Systems
Volume14
Issue number10
DOIs
Publication statusPublished - 2008 Oct

Keywords

  • Evolutionary algorithm
  • Human-like movement
  • Humanoid
  • Imitation learning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Applied Mathematics

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