Angus Mike, Buchanan Edgar, Le Goff Léni K, Hart Emma, Eiben Agoston E, De Carlo Matteo, Winfield Alan F, Hale Matthew F, Woolley Robert, Timmis Jon, Tyrrell Andy M
School of Physics, Engineering and Technology, University of York, York, United Kingdom.
School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom.
Front Robot AI. 2023 Aug 21;10:1206055. doi: 10.3389/frobt.2023.1206055. eCollection 2023.
The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.
进化机器人学领域提供了自主生成机器人的可能性,即通过对具有不同配置的连续几代机器人进行迭代优化,直到找到一个高性能的候选机器人,使其适应所需任务。实际制造如此多机器人所需的高昂时间和成本意味着,大多数进化机器人学工作是在模拟环境中进行的,但要将进化后的机器人应用于现实世界的问题,就必须在硬件中实现,这带来了新的挑战。本文详细探讨了一个用于实现多样化进化机器人机体的示例系统的设计,特别是它如何与进化过程相互作用。我们发现硬件实现的每个方面都会引入改变进化空间的约束,探索硬件约束与进化之间的这种相互作用是本文的关键贡献。在模拟中,可以实现并评估任何能用合适的基因表示定义的机器人,但在硬件中,诸如制造/装配约束和电力供应等现实世界的限制意味着许多这样的机器人无法制造出来,或者在运行中会出现故障。这就带来了一个新的挑战,即如何将进化过程限制在可进化表型空间内,使其仅存在于实际可行的区域:可行表型空间。为解决这个问题引入了表型过滤和修复方法,但发现这些方法会降低机器人种群的多样性,并阻碍对探索空间的遍历。此外,发现硬件约束所允许的自由度与目标环境中最有用的形态变化类型不太匹配。因此,进化过程生成具有有效适应性的机器人的能力大大降低。由此得出的结论有两个。1)为进化机器人设计硬件平台需要不同的思路,所有设计决策都应参照其对可行表型空间的影响来做出。2)仅在模拟中进化机器人而不详细考虑它们将如何在硬件中实现是不够的,因为硬件约束对进化空间有深远影响。