We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Once the human designs have been collected, their impact on the evolutionary process is assessed by replacing a varying number of designs in the initial population with human designs and subsequently running the evolutionary algorithm. Our results suggest that a balance of random and hand-designed initial solutions provides the best performance for the problems considered, and that human designs are most valuable when the problem is intuitive. The influence of human design in an evolutionary algorithm is a highly understudied area, and the insights in this paper may be valuable to the area of AI-based design more generally.
翻译:我们研究将人造设计注入进化机器人实验初始群的影响,随后通过遗传算法和MAP-Elites对机器人群进行优化。首先,人类参与者通过图形前端互动,探索直接分离的断腿机器人设计空间,并尝试通过直觉和试验和试验与测试相结合,在一系列环境中运行良好,来生产机器人。产生环境时,其相应的高性能机器人设计从直觉到复杂和难以掌握。一旦收集了人类设计,它们对于进化过程的影响就通过以人类设计取代初始群中不同数量的设计来评估。我们的结果表明,随机和手工设计的初始解决方案的平衡为所考虑的问题提供了最佳的性能,当问题直观时,人类设计最为宝贵。人类在进化算法中的设计影响是一个非常低的方面,而本文中的洞察力对于更广泛的AI型设计领域可能很有价值。