  Performing agile navigation with four-legged robots is a challenging task due
to the highly dynamic motions, contacts with various parts of the robot, and
the limited field of view of the perception sensors. In this paper, we propose
a fully-learned approach to train such robots and conquer scenarios that are
reminiscent of parkour challenges. The method involves training advanced
locomotion skills for several types of obstacles, such as walking, jumping,
climbing, and crouching, and then using a high-level policy to select and
control those skills across the terrain. Thanks to our hierarchical
formulation, the navigation policy is aware of the capabilities of each skill,
and it will adapt its behavior depending on the scenario at hand. Additionally,
a perception module is trained to reconstruct obstacles from highly occluded
and noisy sensory data and endows the pipeline with scene understanding.
Compared to previous attempts, our method can plan a path for challenging
scenarios without expert demonstration, offline computation, a priori knowledge
of the environment, or taking contacts explicitly into account. While these
modules are trained from simulated data only, our real-world experiments
demonstrate successful transfer on hardware, where the robot navigates and
crosses consecutive challenging obstacles with speeds of up to two meters per
second. The supplementary video can be found on the project website:
https://sites.google.com/leggedrobotics.com/agile-navigation
