A prominent challenge in the field of robotics is manipulation of flexible objects. One major factor that makes this task difficult is the complex dynamics emerging from its high-dimensional structure. This argues against the use of popular optimization-based approaches, which scale poorly with system dimension (the “curse of dimensionality”). Nevertheless, almost indifferent to this complexity, humans handle it on a daily basis, without any apparent difficulty.Inspired by human motor control, we propose that encoding movements based on dynamic primitives can simplify the task of manipulating flexible objects and provides a way around the curse of dimensionality. Using an extreme example — manipulating a whip — we tested in simulation whether targets at various locations could be reached with a whip by using a controller based on dynamic primitives. Regardless of the target location, this approach successfully managed the complexity of a 54 degree-of-freedom system (yielding a 108-dimensional state-space representation) and identified an upper-limb movement that achieved the task. This approach did not require a detailed model of the whip, which thereby significantly simplified the computational complexity of the control task. We believe that this approach may facilitate robotic manipulation of flexible materials, and in general afford a simplified way to control dynamically complex objects.