4D printing (4DP) enables researchers and developers to fabricate soft robots in a more repeatable and integrated manner. The employment of smart materials also reinforces the performance and flexibility of the robot. However, compared with the traditional rigid robots, the 4D-printed soft robots normally have high nonlinearity and time-variant dynamics, which makes the modeling of these robots challenging. Furthermore, as an inherent disadvantage of all soft robots, they also suffer from low actuation precision and poor controllability. This chapter reviews the efforts in modeling and control of 4D-printed soft robots and then summarizes the available approaches. Then, this chapter will compare these approaches based on a 4D-printed actuator using temperature-stimulated shape memory polymers. It is demonstrated that the learning-based methods have great potential in performing better modeling and control of the 4D-printed robots compared with traditional alternatives.