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)

Abstract

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)
Volume17
Edition1 PART 1
DOIs
Publication statusPublished - 2008 Dec 1
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 2008 Jul 62008 Jul 11

Other

Other17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of
CitySeoul
Period08/7/608/7/11

Fingerprint

Evolutionary algorithms
Robots
Torque
Kinetics
Experiments

Keywords

  • 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) https://doi.org/10.3182/20080706-5-KR-1001.4258

Imitation learning of robot movement using evolutionary algorithm. / Park, Galam; Ra, Syungkwon; Kim, Changhwan; Song, Jae-Bok.

IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 17 1 PART 1. ed. 2008.

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

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 edn, vol. 17, 17th World Congress, International Federation of Automatic Control, IFAC, Seoul, Korea, Republic of, 08/7/6. https://doi.org/10.3182/20080706-5-KR-1001.4258
Park G, Ra S, Kim C, Song J-B. Imitation learning of robot movement using evolutionary algorithm. In IFAC Proceedings Volumes (IFAC-PapersOnline). 1 PART 1 ed. Vol. 17. 2008 https://doi.org/10.3182/20080706-5-KR-1001.4258
Park, Galam ; Ra, Syungkwon ; Kim, Changhwan ; Song, Jae-Bok. / Imitation learning of robot movement using evolutionary algorithm. IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 17 1 PART 1. ed. 2008.
@inproceedings{713fc44f82b149bab48109dc1084a575,
title = "Imitation learning of robot movement using evolutionary algorithm",
abstract = "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.",
keywords = "Ubiquitous robotic companion",
author = "Galam Park and Syungkwon Ra and Changhwan Kim and Jae-Bok Song",
year = "2008",
month = "12",
day = "1",
doi = "10.3182/20080706-5-KR-1001.4258",
language = "English",
isbn = "9783902661005",
volume = "17",
booktitle = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
edition = "1 PART 1",

}

TY - GEN

T1 - Imitation learning of robot movement using evolutionary algorithm

AU - Park, Galam

AU - Ra, Syungkwon

AU - Kim, Changhwan

AU - Song, Jae-Bok

PY - 2008/12/1

Y1 - 2008/12/1

N2 - 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.

AB - 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.

KW - Ubiquitous robotic companion

UR - http://www.scopus.com/inward/record.url?scp=79961019977&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79961019977&partnerID=8YFLogxK

U2 - 10.3182/20080706-5-KR-1001.4258

DO - 10.3182/20080706-5-KR-1001.4258

M3 - Conference contribution

AN - SCOPUS:79961019977

SN - 9783902661005

VL - 17

BT - IFAC Proceedings Volumes (IFAC-PapersOnline)

ER -