  Bipedal robots do not perform well as humans since they do not learn to walk
like we do. In this paper we propose a method to train a bipedal robot to
perform some basic movements with the help of imitation learning (IL) in which
an instructor will perform the movement and the robot will try to mimic the
instructor movement. To the best of our knowledge, this is the first time we
train the robot to perform movements with a single video of the instructor and
as the training is done based on joint angles the robot will keep its joint
angles always in physical limits which in return help in faster training. The
joints of the robot are identified by OpenPose architecture and then joint
angle data is extracted with the help of angle between three points resulting
in a noisy solution. We smooth the data using Savitzky-Golay filter and
preserve the Simulatore data anatomy. An ingeniously written Deep Q Network
(DQN) is trained with experience replay to make the robot learn to perform the
movements as similar as the instructor. The implementation of the paper is made
publicly available.
