human-like catching motion of humanoid using evolutionary algorithm(EA)-based imitation learning
- human-like catching motion of humanoid using evolutionary algorithm(EA)-based imitation learning
- 박가람; 김강건; 김창환; 정문호; 유범재; 나성권
- humanoid; imitation learning
- Issue Date
- IEEE International Symposium on Robot and Human Interactive Communication
- , 809-815
- A framework to generate a human-like arm motion
of a humanoid robot using an Evolutionary Algorithm(EA)-
based imitation learning is proposed. The framework consists of
two processes, imitation learning of human arm motions and
real-time generating of a human-like arm motion using the
motion database evolved in the learning process. The imitation
learning builds the database for the humanoid robot that
is initially converted from human motion capture data and
then evolved using a genetic operator based on a Principal
Component Analysis (PCA) in an evolutionary algorithm. This
evolution process also considers the minimizing of joint torques
in the robot. The database is then used to generate humanoid
robot’s arm motions in real-time, which look like human’s
and require minimal torques. The framework is examined
for humanoid robot to reach its arms for catching a ball.
Additionally, the inverse kinematics problem to determine the
final posture of 6-DOF robot arm with a waist for the task of
catching a ball, is proposed.
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- KIST Publication > Conference Paper
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