  Learning or identifying dynamics from a sequence of high-dimensional
observations is a difficult challenge in many domains, including reinforcement
learning and control. The problem has recently been studied from a generative
perspective through latent dynamics: high-dimensional observations are embedded
into a lower-dimensional space in which the dynamics can be learned. Despite
some successes, latent dynamics models have not yet been applied to real-world
robotic systems where learned representations must be robust to a variety of
perceptual confounds and noise sources not seen during training. In this paper,
we present a method to jointly learn a latent state representation and the
associated dynamics that is amenable for long-term planning and closed-loop
control under perceptually difficult conditions. As our main contribution, we
describe how our representation is able to capture a notion of heteroscedastic
or input-specific uncertainty at test time by detecting novel or
out-of-distribution (OOD) inputs. We present results from prediction and
control experiments on two image-based tasks: a simulated pendulum balancing
task and a real-world robotic manipulator reaching task. We demonstrate that
our model produces significantly more accurate predictions and exhibits
improved control performance, compared to a model that assumes homoscedastic
uncertainty only, in the presence of varying degrees of input degradation.
