  There has been an increasing interest in 3D indoor navigation, where a robot
in an environment moves to a target according to an instruction. To deploy a
robot for navigation in the physical world, lots of training data is required
to learn an effective policy. It is quite labour intensive to obtain sufficient
real environment data for training robots while synthetic data is much easier
to construct by rendering. Though it is promising to utilize the synthetic
environments to facilitate navigation training in the real world, real
environment are heterogeneous from synthetic environment in two aspects. First,
the visual representation of the two environments have significant variances.
Second, the houseplans of these two environments are quite different. Therefore
two types of information, i.e. visual representation and policy behavior, need
to be adapted in the reinforcement model. The learning procedure of visual
representation and that of policy behavior are presumably reciprocal. We
propose to jointly adapt visual representation and policy behavior to leverage
the mutual impacts of environment and policy. Specifically, our method employs
an adversarial feature adaptation model for visual representation transfer and
a policy mimic strategy for policy behavior imitation. Experiment shows that
our method outperforms the baseline by 19.47% without any additional human
annotations.
