  One of the main challenges in autonomous robotic exploration and navigation
in unknown and unstructured environments is determining where the robot can or
cannot safely move. A significant source of difficulty in this determination
arises from stochasticity and uncertainty, coming from localization error,
sensor sparsity and noise, difficult-to-model robot-ground interactions, and
disturbances to the motion of the vehicle. Classical approaches to this problem
rely on geometric analysis of the surrounding terrain, which can be prone to
modeling errors and can be computationally expensive. Moreover, modeling the
distribution of uncertain traversability costs is a difficult task, compounded
by the various error sources mentioned above. In this work, we take a
principled learning approach to this problem. We introduce a neural network
architecture for robustly learning the distribution of traversability costs.
Because we are motivated by preserving the life of the robot, we tackle this
learning problem from the perspective of learning tail-risks, i.e. the
Conditional Value-at-Risk (CVaR). We show that this approach reliably learns
the expected tail risk given a desired probability risk threshold between 0 and
1, producing a traversability costmap which is more robust to outliers, more
accurately captures tail risks, and is more computationally efficient, when
compared against baselines. We validate our method on data collected a legged
robot navigating challenging, unstructured environments including an abandoned
subway, limestone caves, and lava tube caves.
