  Learning effective visuomotor policies for robots purely from data is
challenging, but also appealing since a learning-based system should not
require manual tuning or calibration. In the case of a robot operating in a
real environment the training process can be costly, time-consuming, and even
dangerous since failures are common at the start of training. For this reason,
it is desirable to be able to leverage \textit{simulation} and
\textit{off-policy} data to the extent possible to train the robot. In this
work, we introduce a robust framework that plans in simulation and transfers
well to the real environment. Our model incorporates a gradient-descent based
planning module, which, given the initial image and goal image, encodes the
images to a lower dimensional latent state and plans a trajectory to reach the
goal. The model, consisting of the encoder and planner modules, is trained
through a meta-learning strategy in simulation first. We subsequently perform
adversarial domain transfer on the encoder by using a bank of unlabelled but
random images from the simulation and real environments to enable the encoder
to map images from the real and simulated environments to a similarly
distributed latent representation. By fine tuning the entire model (encoder +
planner) with far fewer real world expert demonstrations, we show successful
planning performances in different navigation tasks.
