  Modern reinforcement learning methods suffer from low sample efficiency and
unsafe exploration, making it infeasible to train robotic policies entirely on
real hardware. In this work, we propose to address the problem of sim-to-real
domain transfer by using meta learning to train a policy that can adapt to a
variety of dynamic conditions, and using a task-specific trajectory generation
model to provide an action space that facilitates quick exploration. We
evaluate the method by performing domain adaptation in simulation and analyzing
the structure of the latent space during adaptation. We then deploy this policy
on a KUKA LBR 4+ robot and evaluate its performance on a task of hitting a
hockey puck to a target. Our method shows more consistent and stable domain
adaptation than the baseline, resulting in better overall performance.
