  We study the constrained linear quadratic regulator with unknown dynamics,
addressing the tension between safety and exploration in data-driven control
techniques. We present a framework which allows for system identification
through persistent excitation, while maintaining safety by guaranteeing the
satisfaction of state and input constraints. This framework involves a novel
method for synthesizing robust constraint-satisfying feedback controllers,
leveraging newly developed tools from system level synthesis. We connect
statistical results with cost sub-optimality bounds to give non-asymptotic
guarantees on both estimation and controller performance.
