论文标题
使用单个基因组共同发展的形态和软机器人控制
Co-evolving morphology and control of soft robots using a single genome
论文作者
论文摘要
在模拟软机器人时，它们的形态和控制器都在任务绩效中起着重要作用。本文介绍了一种在同一过程中共同进化这两个组件的新方法。我们通过使用HyperNeat算法在一个通道中生成两个独立的神经网络来做到这一点，一个负责机器人体结构的设计，另一个负责机器人的控制。 我们的方法和大多数现有方法之间的关键区别在于，它不会将形态学和控制器的发展视为单独的过程。与自然相似，我们的方法既从单个基因组中得出了代理的“大脑”和“身体”，并将它们共同发展在一起。尽管我们的方法更现实，并且不需要在进化过程中任意将过程分离，但它也使问题更加复杂，因为该单个基因组的搜索空间变得更大，并且对基因组的任何突变都会同时影响“大脑”和“身体”。 此外，我们提出了一种新的物种函数，该函数既考虑基因型距离，又要考虑整洁的标准以及机器人体之间的相似性。通过使用此功能，具有非常不同物体的代理更有可能在不同的物种中，这允许具有不同形态的机器人具有更专业的控制器，因为它们不会与其他与其他机器人截然不同的机器人。 我们评估了关于四个任务的提出方法，并观察到，即使搜索空间较大，单个基因组也可以使进化过程在身体和控制的基因组中相比更快。我们人群中的药物还显示出具有高规律性的形态，并且能够协调体素以产生必要运动的控制器。
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.
