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 language | English |
|---|---|
| Title of host publication | IFAC Proceedings Volumes (IFAC-PapersOnline) |
| Volume | 17 |
| Edition | 1 PART 1 |
| DOIs | |
| Publication status | Published - 2008 Dec 1 |
| Event | 17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of Duration: 2008 Jul 6 → 2008 Jul 11 |
Other
| Other | 17th World Congress, International Federation of Automatic Control, IFAC |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 08/7/6 → 08/7/11 |
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Keywords
- Ubiquitous robotic companion
ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
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 proceeding › Conference contribution
}
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 -