  This paper presents a framework for the safety-critical control of robotic
systems, when safety is defined on safe regions in the configuration space. To
maintain safety, we synthesize a safe velocity based on control barrier
function theory without relying on a -- potentially complicated --
high-fidelity dynamical model of the robot. Then, we track the safe velocity
with a tracking controller. This culminates in model-free safety critical
control. We prove theoretical safety guarantees for the proposed method.
Finally, we demonstrate that this approach is application-agnostic. We execute
an obstacle avoidance task with a Segway in high-fidelity simulation, as well
as with a Drone and a Quadruped in hardware experiments.
