  Teaching an anthropomorphic robot from human example offers the opportunity
to impart humanlike qualities on its movement. In this work we present a
reinforcement learning based method for teaching a real world bipedal robot to
perform movements directly from human motion capture data. Our method
seamlessly transitions from training in a simulation environment to executing
on a physical robot without requiring any real world training iterations or
offline steps. To overcome the disparity in joint configurations between the
robot and the motion capture actor, our method incorporates motion re-targeting
into the training process. Domain randomization techniques are used to
compensate for the differences between the simulated and physical systems. We
demonstrate our method on an internally developed humanoid robot with movements
ranging from a dynamic walk cycle to complex balancing and waving. Our
controller preserves the style imparted by the motion capture data and exhibits
graceful failure modes resulting in safe operation for the robot. This work was
performed for research purposes only.
