arXiv:2606.07424v2 Announce Type: replace Abstract: Designing high-performance legged robots requires jointly optimizing morphology and control. Model-free reinforcement learning (RL) enables learning complex controllers during co-design, but repeatedly training policies across candidate morphologies makes design optimization computationally expensive. Existing approaches can amortize policy learning across morphologies, but a single shared policy tends to converge toward one control strategy regardless of morphology, which we refer to as behavioral diversity collapse. We introduce Gaussian Evolutionary Specialists (GES), an RL-based co-design framework that maintains a population of specialist policies whose design-space territories evolve according to their relative control performance. We evaluate GES on the Buoyancy-Assisted Light Legged Unit (BALLU) robot for obstacle traversal and ramp walking over design spaces of up to four dimensions. GES achieves higher best design performance than Monolithic and Mixture-of-Experts baselines, with gains of 5.0-9.0 cm in obstacle height and 10.0-12.7 points in ramp grade. For the same 200 design-optimization evaluations, GES reduces wall-clock co-design time by 67.4% compared with retraining a policy for every candidate. Finally, teleoperated hardware experiments evaluate the optimized BALLU morphology, which traverses obstacles of up to 24 cm, compared with 8 cm on the original design.