Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulators, represent a valid tool to overcome this issue. In this work, we investigate algorithmic solutions to improve the Quality Diversity of co-evolved designs of Tensegrity Soft Modular Robots (TSMRs) for two robotic tasks, namely goal reaching and squeezing trough a narrow passage. To this aim, we use three different EAs, i.e., MAP-Elites and two custom algorithms: one based on Viability Evolution (ViE) and NEAT (ViE-NEAT), the other named Double Map MAP-Elites (DM-ME) and devised to seek diversity while co-evolving robot morphologies and neural network (NN)-based controllers. In detail, DM-ME extends MAP-Elites in that it uses two distinct feature maps, referring to morphologies and controllers respectively, and integrates a mechanism to automatically define the NN-related feature descriptor. Considering the fitness, in the goal-reaching task ViE-NEAT outperforms MAP-Elites and results equivalent to DM-ME. Instead, when considering diversity in terms of "illumination" of the feature space, DM-ME outperforms the other two algorithms on both tasks, providing a richer pool of possible robotic designs, whereas ViE-NEAT shows comparable performance to MAP-Elites on goal reaching, although it does not exploit any map.

中文翻译：

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在形态协同设计和软张力模块化机器人控制中寻求质量多样性

由于形态和控制器之间的平凡互动，设计最佳的软模块化机器人非常困难。进化算法（EA）与物理模拟器相结合，是克服此问题的有效工具。在这项工作中，我们研究了算法解决方案，以改善Tensegrity软模块化机器人（TSMR）共同开发的设计的质量多样性，以完成两项机器人任务，即目标到达和通过狭窄通道的挤压。为此，我们使用了三种不同的EA，即MAP-Elites和两种自定义算法：一种基于生存力进化（ViE）和NEAT（ViE-NEAT），另一种称为Double Map MAP-Elites（DM-ME）和旨在寻求多样性，同时共同进化机器人形态和基于神经网络（NN）的控制器。具体地说，DM-ME通过使用两个不同的特征图来扩展MAP-Elites，分别引用形态和控制器，并集成了自动定义与NN相关的特征描述符的机制。考虑到适合性，在达到目标的任务中，ViE-NEAT胜过MAP-Elites，结果等同于DM-ME。取而代之的是，在考虑特征空间“照度”方面的多样性时，DM-ME在这两项任务上均优于其他两种算法，从而提供了更丰富的可能的机器人设计库，而ViE-NEAT在目标上则表现出与MAP-Elites相当的性能。到达，尽管它没有利用任何地图。