  This work presents Neural Gaits, a method for learning dynamic walking gaits
through the enforcement of set invariance that can be refined episodically
using experimental data from the robot. We frame walking as a set invariance
problem enforceable via control barrier functions (CBFs) defined on the
reduced-order dynamics quantifying the underactuated component of the robot:
the zero dynamics. Our approach contains two learning modules: one for learning
a policy that satisfies the CBF condition, and another for learning a residual
dynamics model to refine imperfections of the nominal model. Importantly,
learning only over the zero dynamics significantly reduces the dimensionality
of the learning problem while using CBFs allows us to still make guarantees for
the full-order system. The method is demonstrated experimentally on an
underactuated bipedal robot, where we are able to show agile and dynamic
locomotion, even with partially unknown dynamics.
