Towards Scaling Search in Robot Co-Design

Robot co-design has begun to scale its search to millions of candidate bodies. Search methods that train a controller for every body can scale by screening candidates with brief training, keeping the highest-scoring body and retraining it fully. We measure the screen's exact gain over a random body as the screen budget, candidate count (up to 128) and total compute vary. Each body in our pools has separately trained screening and final controllers. In three of four design spaces, keeping the best of four after a quarter of the full training budget improves the final score by 0.63 to 0.73 standard deviations over a random body. More candidates add to the gain, and at the same total compute the screen beats training two bodies fully and retraining the better one by 0.27 to 0.29 standard deviations. In a Walker2d variant whose reward pays only for forward travel, the same screen picks bodies that travel about 5% less than a random body. A preregistered test confirmed this harm, and it replicated with new seeds and grew with longer final training. Brief training favors heavy walkers that are weak for their weight, which full training ranks low. The harm fades with more candidates. Continuing this screen's controllers costs less and beats retraining in every pool we trained. In the walker, keeping the best of four after half the training budget instead improves the final score by 0.09 to 0.11 standard deviations over a random body. We release all our pools and code at https://anonymous.4open.science/r/scaling-codesign-search, and any best-of-N screen at our recorded budgets can be scored on the pools without training.