  With the increasing computing power, using data-driven approaches to
co-design a robot's morphology and controller has become a promising way.
However, most existing data-driven methods require training the controller for
each morphology to calculate fitness, which is time-consuming. In contrast, the
dual-network framework utilizes data collected by individual networks under a
specific morphology to train a population network that provides a surrogate
function for morphology optimization. This approach replaces the traditional
evaluation of a diverse set of candidates, thereby speeding up the training.
Despite considerable results, the online training of both networks impedes
their performance. To address this issue, we propose a concurrent network
framework that combines online and offline reinforcement learning (RL) methods.
By leveraging the behavior cloning term in a flexible manner, we achieve an
effective combination of both networks. We conducted multiple sets of
comparative experiments in the simulator and found that the proposed method
effectively addresses issues present in the dual-network framework, leading to
overall algorithmic performance improvement. Furthermore, we validated the
algorithm on a real robot, demonstrating its feasibility in a practical
application.
