Understanding Co-Design: Exploiting the Asymmetry between the Designer and Controller

Robot co-design jointly optimizes morphology and control against the same final objective, but does that mean the two optimizers should behave alike? To answer this, we create a test environment and explore it through the lens of three new metrics: spread, exploration, and generalization. By varying these properties independently, we find that effective co-design requires opposite behavior from each optimizer: the controller requires broad generalization to new designs and suffers from wide sampling, whereas the designer barely benefits from generalization but must sample the design space broadly to find good designs. We show that the source of this asymmetry is due to the sequential structure of co-design in which the body is chosen before the controller acts, and that it can be explained through the Expected Value of Perfect Information. Our test environment also shows that broader morphology search can discover better designs but may simultaneously worsen the selection when the controller cannot evaluate unfamiliar bodies reliably. We observe optimization profiles in existing co-design methods, and combine our findings to construct SCOPER which is tailored to exploit the optimizer asymmetry by maintaining a broad morphological search space, and using representation learning to increase controller generalization. In our experiments, SCOPER demonstrates the value of following these principles by outperforming prior co-design methods task performance by  50% and halving runtime.