  Imitation from observation is the framework of learning tasks by observing
demonstrated state-only trajectories. Recently, adversarial approaches have
achieved significant performance improvements over other methods for imitating
complex behaviors. However, these adversarial imitation algorithms often
require many demonstration examples and learning iterations to produce a policy
that is successful at imitating a demonstrator's behavior. This high sample
complexity often prohibits these algorithms from being deployed on physical
robots. In this paper, we propose an algorithm that addresses the sample
inefficiency problem by utilizing ideas from trajectory centric reinforcement
learning algorithms. We test our algorithm and conduct experiments using an
imitation task on a physical robot arm and its simulated version in Gazebo and
will show the improvement in learning rate and efficiency.
