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.

本文提出了一种结合生成模型和模型不确定性的不确定性感知机器人感知方法，用于处理源自超出分布状态的不确定性。