Learning Humanoid Robot Motions Through Deep Neural Networks
  Controlling a high degrees of freedom humanoid robot is acknowledged as one
of the hardest problems in Robotics. Due to the lack of mathematical models, an
approach frequently employed is to rely on human intuition to design keyframe
movements by hand, usually aided by graphical tools. In this paper, we propose
a learning framework based on neural networks in order to mimic humanoid robot
movements. The developed technique does not make any assumption about the
underlying implementation of the movement, therefore both keyframe and
model-based motions may be learned. The framework was applied in the RoboCup 3D
Soccer Simulation domain and promising results were obtained using the same
network architecture for several motions, even when copying motions from
another teams.
