 Robots are some of the most complex technological systems on Earth, but some scientists say they don't have to be. A new automation model could do away with the complicated mathematical machinery that normally makes robots tick, and replace it with a more intuitive and ultimately simpler guiding system. Before a robot is even assembled, the rules for its behavior are laid out in explicit mathematical detail. There are rules for maintaining the right temperature, for routing power, and for navigating in space. But all these rules make for a rather unintuitive way of approaching a task. Take driving, for example. People monitor speed and distance, but mostly drive by feel for where the car is and where they want it to go. The key is viewing the control system as a whole, rather than considering the cause and effect relationship between the many parts. This principle forms the basis of a new model of automation. Whether they're tracking physical distance, voltage, or energy, under this new model, machines can continuously compare their actual and desired trajectories to reach a target state. Such an active control system can substitute for the more static, rule-based design of many robots and could allow autonomous vehicles to operate under uncertain conditions, such as tracking an unknown target in the absence of GPS. To help implement this technique in practice, researchers called upon a brand of calculus, known as fractional calculus. Unlike the classical version used in most control systems, fractional calculus provides a way of accounting for the full history of a robot's behavior. In effect, a fractional control system remembers all past errors to determine a robot's best next move. Simulations of an automobile control system taking this approach showed that the system landed on the ideal trajectory faster and more smoothly than a conventional control model. Future work is needed to extend this new framework to other, more complex control scenarios. Scenarios like driving through a strong crosswind that feature outside disturbances. But the outlook appears promising for a technique that can simplify the increasingly complex world of automation.