It's important for a robot not just to learn a task optimally, but to complete it without going off the rails of reasonable expectations. Until now, roboticists could have one or the other. Aerospace engineering researchers in The Grainger College of Engineering, University of Illinois Urbana-Champaign, combined reinforcement learning with control theory to guarantee robot stability and improve tracking performance by up to 15% compared with existing methods.