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.
翻译:设计最佳软模块机器人非常困难, 原因是形态学和控制器之间的非三角互动。 进化算法( EAs) 和物理模拟器是解决这一问题的有效工具。 在这项工作中, 我们调查算法解决方案, 以改善感官软模块机器人( TSMRs) 共演化设计的质量多样性, 用于两项机器人任务, 即: 达到目标并用狭小通道挤压。 为此, 我们使用三种不同的计算器, 即 MAP- Elites 和 两种定制算法: 一种基于易变性演化( ViE- NEAT) 和 NEAT( ViE- NE- NEAT), 另一种名为双映制地图- Elites( DM-ME ), 设计以寻求多样性, 而双演动机器人形态和神经网络控制器。 详细来说, DMME 扩展了 MAP- Elites, 它使用两种不同的特性图, 分别指着变色体和调控器, 并整合了VE- AT 的可比较性任务, 在DMARDMA 中, 格式中, 提供与目标相关的特性分析结果结果,,,,, 考虑其他的功能- destrualformations- div- somen- somen- div- div exmalmentalmentalmentaldal ex ex ex ex ex exportmentalus des 。