In evolutionary robotics, jointly optimising the design and the controller of robots is a challenging task due to the huge complexity of the solution space formed by the possible combinations of body and controller. We focus on the evolution of robots that can be physically created rather than just simulated, in a rich morphological space that includes a voxel-based chassis, wheels, legs and sensors. On the one hand, this space offers a high degree of liberty in the range of robots that can be produced, while on the other hand introduces a complexity rarely dealt with in previous works relating to matching controllers to designs and in evolving closed-loop control. This is usually addressed by augmenting evolution with a learning algorithm to refine controllers. Although several frameworks exist, few have studied the role of the \textit{evolutionary dynamics} of the intertwined `evolution+learning' processes in realising high-performing robots. We conduct an in-depth study of the factors that influence these dynamics, specifically: synchronous vs asynchronous evolution; the mechanism for replacing parents with offspring, and rewarding goal-based fitness vs novelty via selection. Results show that asynchronicity combined with goal-based selection and a `replace worst' strategy results in the highest performance.

翻译：在进化机器人学中，联合优化机器人的设计与控制器是一项具有挑战性的任务，原因在于身体与控制器可能组合形成的解空间极为复杂。我们聚焦于可在物理世界中实际制造（而非仅限于模拟）的机器人进化过程，其所涉及的高维形态空间包含基于体素的底盘、轮子、腿部和传感器。一方面，该空间为可生成的机器人类型提供了高度自由度；另一方面，它也引入了以往研究中鲜有充分探讨的复杂性，包括控制器与设计的匹配以及闭环控制的进化等问题。通常，解决途径是在进化过程中引入学习算法以优化控制器。尽管已有若干框架存在，但鲜有研究深入分析交织的“进化+学习”过程中所谓“进化动态”对实现高性能机器人的作用。我们系统探究了影响这些动态的关键因素，具体包括：同步进化与异步进化、子代取代亲代的机制、以及基于目标适应度与基于新颖性选择策略的差异。结果表明，异步机制结合基于目标的选择与“替换最差”策略可获得最优性能。