  We propose improving the cross-target and cross-scene generalization of
visual navigation through learning an agent that is guided by conceiving the
next observations it expects to see. This is achieved by learning a variational
Bayesian model, called NeoNav, which generates the next expected observations
(NEO) conditioned on the current observations of the agent and the target view.
Our generative model is learned through optimizing a variational objective
encompassing two key designs. First, the latent distribution is conditioned on
current observations and the target view, leading to a model-based,
target-driven navigation. Second, the latent space is modeled with a Mixture of
Gaussians conditioned on the current observation and the next best action. Our
use of mixture-of-posteriors prior effectively alleviates the issue of
over-regularized latent space, thus significantly boosting the model
generalization for new targets and in novel scenes. Moreover, the NEO
generation models the forward dynamics of agent-environment interaction, which
improves the quality of approximate inference and hence benefits data
efficiency. We have conducted extensive evaluations on both real-world and
synthetic benchmarks, and show that our model consistently outperforms the
state-of-the-art models in terms of success rate, data efficiency, and
generalization.
