Traversing terrain with good traction is crucial for achieving fast off-road
navigation. Instead of manually designing costs based on terrain features,
existing methods learn terrain properties directly from data via
self-supervision, but challenges remain to properly quantify and mitigate risks
due to uncertainties in learned models. This work efficiently quantifies both
aleatoric and epistemic uncertainties by learning discrete traction
distributions and probability densities of the traction predictor's latent
features. Leveraging evidential deep learning, we parameterize Dirichlet
distributions with the network outputs and propose a novel uncertainty-aware
squared Earth Mover's distance loss with a closed-form expression that improves
learning accuracy and navigation performance. The proposed risk-aware planner
simulates state trajectories with the worst-case expected traction to handle
aleatoric uncertainty, and penalizes trajectories moving through terrain with
high epistemic uncertainty. Our approach is extensively validated in simulation
and on wheeled and quadruped robots, showing improved navigation performance
compared to methods that assume no slip, assume the expected traction, or
optimize for the worst-case expected cost.Comment: Under review. Journal extension for arXiv:2210.00153. Project
website: https://xiaoyi-cai.github.io/evora