  Motion planning in off-road environments requires reasoning about both the
geometry and semantics of the scene (e.g., a robot may be able to drive through
soft bushes but not a fallen log). In many recent works, the world is
classified into a finite number of semantic categories that often are not
sufficient to capture the ability (i.e., the speed) with which a robot can
traverse off-road terrain. Instead, this work proposes a new representation of
traversability based exclusively on robot speed that can be learned from data,
offers interpretability and intuitive tuning, and can be easily integrated with
a variety of planning paradigms in the form of a costmap. Specifically, given a
dataset of experienced trajectories, the proposed algorithm learns to predict a
distribution of speeds the robot could achieve, conditioned on the environment
semantics and commanded speed. The learned speed distribution map is converted
into costmaps with a risk-aware cost term based on conditional value at risk
(CVaR). Numerical simulations demonstrate that the proposed risk-aware planning
algorithm leads to faster average time-to-goals compared to a method that only
considers expected behavior, and the planner can be tuned for slightly slower,
but less variable behavior. Furthermore, the approach is integrated into a full
autonomy stack and demonstrated in a high-fidelity Unity environment and is
shown to provide a 30\% improvement in the success rate of navigation.
