Exposing an Evolutionary Algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this article, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.

翻译：在进化机器人中，为使进化算法生成的机器人控制器具有鲁棒性并能跨越现实差距，必须使其暴露于可变条件中。然而，我们目前缺乏分析和理解影响进化过程的形态变异条件影响的方法，因此难以选择合适的变异范围。所谓形态条件，是指机器人的初始状态，以及运行过程中因噪声导致的传感器读数变化。本文提出一种测量形态变异影响的方法，并分析变异幅度、引入方式与进化代理性能及鲁棒性之间的关系。结果表明：（i）进化算法能容忍具有极高影响的形态变异；（ii）影响代理动作的变异比影响代理或环境初始状态的变异更易被容忍；（iii）通过多次评估提高适应度测度的准确性并非总是有效。此外，我们的结果还表明，形态变异能够在可变和不变条件下均生成表现更优的解决方案。