  Learning to control robots directly based on images is a primary challenge in
robotics. However, many existing reinforcement learning approaches require
iteratively obtaining millions of robot samples to learn a policy, which can
take significant time. In this paper, we focus on learning a realistic world
model capturing the dynamics of scene changes conditioned on robot actions. Our
dreaming model can emulate samples equivalent to a sequence of images from the
actual environment, technically by learning an action-conditioned future
representation/scene regressor. This allows the agent to learn action policies
(i.e., visuomotor policies) by interacting with the dreaming model rather than
the real-world. We experimentally confirm that our dreaming model enables robot
learning of policies that transfer to the real-world.
