  Deep learning provides a powerful tool for machine perception when the
observations resemble the training data. However, real-world robotic systems
must react intelligently to their observations even in unexpected
circumstances. This requires a system to reason about its own uncertainty given
unfamiliar, out-of-distribution observations. Approximate Bayesian approaches
are commonly used to estimate uncertainty for neural network predictions, but
can struggle with out-of-distribution observations. Generative models can in
principle detect out-of-distribution observations as those with a low estimated
density. However, the mere presence of an out-of-distribution input does not by
itself indicate an unsafe situation. In this paper, we present a method for
uncertainty-aware robotic perception that combines generative modeling and
model uncertainty to cope with uncertainty stemming from out-of-distribution
states. Our method estimates an uncertainty measure about the model's
prediction, taking into account an explicit (generative) model of the
observation distribution to handle out-of-distribution inputs. This is
accomplished by probabilistically projecting observations onto the training
distribution, such that out-of-distribution inputs map to uncertain
in-distribution observations, which in turn produce uncertain task-related
predictions, but only if task-relevant parts of the image change. We evaluate
our method on an action-conditioned collision prediction task with both
simulated and real data, and demonstrate that our method of projecting
out-of-distribution observations improves the performance of four standard
Bayesian and non-Bayesian neural network approaches, offering more favorable
trade-offs between the proportion of time a robot can remain autonomous and the
proportion of impending crashes successfully avoided.
