  In nature, animals with exceptional locomotion abilities, such as cougars,
often possess asymmetric fore and hind legs, with their powerful hind legs
acting as reservoirs of energy for leaps. This observation inspired us: could
optimize the leg length of quadruped robots endow them with similar locomotive
capabilities? In this paper, we propose an approach that co-optimizes the
mechanical structure and control policy to boost the locomotive prowess of
quadruped robots. Specifically, we introduce a novel pretraining-finetuning
framework, which not only guarantees optimal control strategies for each
mechanical candidate but also ensures time efficiency. Additionally, we have
devised an innovative training method for our pretraining network, integrating
spatial domain randomization with regularization methods, markedly improving
the network's generalizability. Our experimental results indicate that the
proposed pretraining-finetuning framework significantly enhances the overall
co-design performance with less time consumption. Moreover, the co-design
strategy substantially exceeds the conventional method of independently
optimizing control strategies, further improving the robot's locomotive
performance and providing an innovative approach to enhancing the extreme
parkour capabilities of quadruped robots.
