  Modern nonlinear control theory seeks to endow systems with properties of
stability and safety, and have been deployed successfully in multiple domains.
Despite this success, model uncertainty remains a significant challenge in
synthesizing safe controllers, leading to degradation in the properties
provided by the controllers. This paper develops a machine learning framework
utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it
impact the safe behavior of a system. This approach iteratively collects data
and updates a controller, ultimately achieving safe behavior. We validate this
method in simulation and experimentally on a Segway platform.
