When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.
翻译:当模拟软机器人时, 它们的形态和控制器在任务性能中扮演着重要角色。 本文引入了一种新的方法, 在同一过程中共演进这两个元素。 我们这样做的方法是使用超NEAT算法来生成两个不同的神经网络, 一个负责机器人身体结构的设计, 另一个负责机器人控制。 我们的方法和大多数现有方法之间的关键区别在于它并不将形态学和控制器的开发作为不同的过程。 与自然相似, 我们的方法从一个单一基因组的代理体的“ 呼吸” 和“ 体” 中产生一种“ 体”, 并一起演化它们。 虽然我们的方法更现实, 不需要在进化过程中任意分离两个神经网络, 一个负责机器人身体结构的设计, 一个负责机器人结构结构的设计, 并且同时影响“ 体” 。 此外, 我们提出了一个新的观察功能, 既考虑基因学距离, 也是 NEAT 的标定标准, 也同时将两者的“体体态” 一起演算。 虽然我们的方法并不相同, 但是, 当机器人的机体的机体的机体的机体的机体的机体变化也更具有不同时, 的机体变化的机体的机体的机体的机体运动会产生不同, 我们的机体的机体的机体的机体运动的机体的机体运动的机体运动的机体运动的机体运动的机体运动的机体和机体运动的机体运动的机体的机体也具有不同。