Abstract—The imitation between different types of robots
remains an unsolved task for a long time. The assignment of
the correct angles to each joint is critical for robot motion.
However, different robots have different structures, thus this
discrepancy causes a difficulty when converting a motion to
another type of robot. For solving this problem, we propose a
GA-based method that can find the conversion matrix needed
to map joint angles of one robot to another. There are two
objectives to consider when creating an imitation; reducing the
difference between the ideal imitation and the converted
imitation and keeping the stability. Three experiments were
conducted; a stable experiment, an unstable experiment and a
double learning experiment. As a result, the double experiment
showed a high concordance rate of 93.5%, the highest stability
and the fastest speed of all experiments. These results show
great promise for the proposed method as a way to realize
motion imitation between different types of robots.
Index Terms—Robot, imitation, motion planning, humanoid
robot, genetic algorithms, autonomous learning.
Mari Nishiyama is with the Department of Electrical and Electronic
Engineering, the University of Tokyo, Japan (e-mail: nishiyama@iba.t.utokyo.
ac.jp).
Hitoshi Iba is with the Graduate School of Information Science and
Technology, the University of Tokyo, Japan (e-mail: iba@iba.t.utokyo.
ac.jp).
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Cite: Mari Nishiyama and Hitoshi Iba, "Converting Motion between Different Types of Humanoid Robots Using Genetic Algorithms," International Journal of Computer Theory and Engineering vol. 7, no. 2, pp. 97-102, 2015.
