Nordmoen Jørgen, Veenstra Frank, Ellefsen Kai Olav, Glette Kyrre
Department of Informatics, University of Oslo, Oslo, Norway.
RITMO, University of Oslo, Oslo, Norway.
Front Robot AI. 2021 Apr 28;8:639173. doi: 10.3389/frobt.2021.639173. eCollection 2021.
In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm-MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm's capability of generating stepping stones for reaching high-performing solutions.
在模块化机器人技术中，模块可以重新配置以改变机器人的形态，使其能够适应特定任务。然而，由于微调控制与形态变化之间存在复杂的关系，这种关系可能会使此类优化无效，因此优化此类机器人的身体和控制是一项艰巨的挑战。这些挑战可能会使许多优化算法陷入局部最优，阻碍朝着更好解决方案的进展。为了解决这一挑战，我们比较了三种不同的进化算法在模块化机器人技术中优化高性能和多样化形态及控制器的能力。我们将两种基于目标的搜索算法（一种带有促进多样性的目标，另一种不带）与一种质量多样性算法——MAP-Elites进行比较。结果表明，MAP-Elites除了能产生最大的形态多样性外，还能够进化出性能最高的解决方案。此外，在将种群转移到新的、更具挑战性的环境时，MAP-Elites在恢复性能方面表现更优。通过分析谱系祖先，我们发现MAP-Elites比其他两种基于目标的搜索算法产生更多样化且性能更高的垫脚石。将种群转移到新环境的实验展示了形态多样性的效用，而对垫脚石的分析表明祖先的多样性与运动任务的最大性能之间存在很强的相关性。总之，这些结果证明了MAP-Elites适用于模块化机器人形态控制搜索这一具有挑战性的任务，并揭示了该算法生成垫脚石以实现高性能解决方案的能力。
