Imitation learning of robot movement using evolutionary algorithm

Galam Park, Syungkwon Ra, Changhwan Kim, Jae Bok Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


This paper presents a new framework to generate human-like movement of a humanoid robot 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 Evolutionary Algorithm and (2) the movement generation of a humanoid robot 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 Evolutionary Algorithm, having characteristics of a humanoid in aspect of minimal torque. The humanoid generates a human-like movement corresponding to a given task in real-time by linearly interpolating the primitive movements in the developed database. The proposed framework is a systematic methodology for a humanoid robot to learn human motions, considering the dynamics of the robot. The experiment of catching a ball thrown by a man is performed to show the feasibility of the proposed framework.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
Edition1 PART 1
Publication statusPublished - 2008 Dec 1
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 2008 Jul 62008 Jul 11


Other17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of



  • Ubiquitous robotic companion

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Park, G., Ra, S., Kim, C., & Song, J. B. (2008). Imitation learning of robot movement using evolutionary algorithm. In IFAC Proceedings Volumes (IFAC-PapersOnline) (1 PART 1 ed., Vol. 17)