  The need for robust control laws is especially important in safety-critical
applications. We propose robust hybrid control barrier functions as a means to
synthesize control laws that ensure robust safety. Based on this notion, we
formulate an optimization problem for learning robust hybrid control barrier
functions from data. We identify sufficient conditions on the data such that
feasibility of the optimization problem ensures correctness of the learned
robust hybrid control barrier functions. Our techniques allow us to safely
expand the region of attraction of a compass gait walker that is subject to
model uncertainty.
