In some evolutionary robotics experiments, evolved robots are transferred from simulation to reality, while sensor/motor data flows back from reality to improve the next transferral. We envision a generalization of this approach: a simulation-to-reality pipeline. In this pipeline, increasingly embodied agents flow up through a sequence of increasingly physically realistic simulators, while data flows back down to improve the next transferral between neighboring simulators; physical reality is the last link in this chain. As a first proof of concept, we introduce a two-link chain: A fast yet low-fidelity (lo-fi) simulator hosts minimally embodied agents, which gradually evolve controllers and morphologies to colonize a slow yet high-fidelity (hi-fi) simulator. The agents are thus physically scaffolded. We show here that, given the same computational budget, these physically scaffolded robots reach higher performance in the hi-fi simulator than do robots that only evolve in the hi-fi simulator, but only for a sufficiently difficult task. These results suggest that a simulation-to-reality pipeline may strike a good balance between accelerating evolution in simulation while anchoring the results in reality, free the investigator from having to prespecify the robot's morphology, and pave the way to scalable, automated, robot-generating systems.