QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms
QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms
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DOI：
10.1109/tevc.2020.3036578
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发表时间：
2020-03
影响因子：
14.3
通讯作者：
David M. Bossens;Danesh Tarapore
中科院分区：
文献类型：
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作者：
David M. Bossens;Danesh Tarapore
在质量多样性算法中，行为多样性度量是决定进化档案质量的关键设计选择。尽管行为多样性传统上是通过描述在单一环境中评估的机器人控制器的观察到的结果行为来获得的，但通常更容易通过引入环境多样性来诱导，即通过操纵评估控制器的环境。本文提出了质量环境多样性（QED）算法，该算法根据环境特征（例如障碍物数量、场地大小以及机器人传感器和执行器特性）的概率分布重复生成随机环境，评估该环境中的控制器，然后根据该环境的特征（环境描述符）来描述控制器。我们的研究在 5 个不同的机器人群基准任务中将 QED 与三个基线特定任务和通用行为描述符进行了比较。对于每项任务，进化档案的质量是根据它们在向群体机器人注入 250 个独特故障后提供高性能补偿行为的能力来评估的。进化后的档案将故障对集群性能的影响平均降低了 2 到 3 倍。通过可视化补偿行为多样性（这里称为有用行为多样性）和故障恢复指标之间的关系来完成对演化档案的定性分析。由此产生的签名表明，由于环境的多样性会导致有用的行为多样性，QED 演化出的档案提供了能够从高影响故障中恢复的机器人群控制器。
In quality-diversity algorithms, the behavioral diversity metric is a key design choice that determines the quality of the evolved archives. Although behavioral diversity is traditionally obtained by describing the observed resulting behavior of robot controllers evaluated in a single environment, it is often more easily induced by introducing environmental diversity, i.e., by manipulating the environments in which the controllers are evaluated. This article proposes quality-environment-diversity (QED), an algorithm that repeatedly generates a random environment according to a probability distribution over environmental features (e.g., number of obstacles, arena size and robot sensor and actuator characteristics), evaluates the controller in that environment, and then describes the controller in terms of the features of that environment, the environment descriptor. Our study compares QED to three baseline task-specific and generic behavioral descriptors, in 5 different robot swarm benchmark tasks. For each task, the quality of the evolved archives is assessed by their capability to provide high-performing compensatory behaviors following injection of 250 unique faults to the robots of the swarm. The evolved archives achieve a median 2- to 3-fold reduction in the impact of the faults on the performance of the swarm. A qualitative analysis of evolved archives is done by visualizing the relation between diversity of compensatory behaviors, here called useful behavioral diversity, and fault recovery metrics. The resulting signatures indicate that, due to the diversity of environments inducing useful behavioral diversity, archives evolved by QED provide robot swarm controllers that are capable of recovering from high-impact faults.