基于L系统的形态进化机器人步态学习
Gait-learning with morphologically evolving robots generated by L-system
摘要：
当机器人的控制器（大脑）和形态（身体）同时进化时，这可能会导致一个问题，即大脑和身体不匹配问题。在本研究中，我们提出了终身学习的解决方案。我们建立了一个系统，模块化机器人可以通过重组和突变创造继承父母身体的后代。关于后代的大脑，我们使用两种方法来创造它们。第一个是进化，这意味着机器人孩子的大脑是从父母那里继承来的。第二种方法是进化加学习，这意味着孩子的大脑也是遗传的，但另外是由一种学习算法- RevDEknn开发的。我们通过在一个名为Revolve的模拟器中运行实验来比较这两种方法，并使用效率，功效和机器人的形态智能进行比较。实验表明，进化加学习方法不仅导致更高的适应度水平，但也有更多的形态进化的机器人。这构成了一个定量的证明，即大脑的变化可以引起身体的变化，导致形态智能的概念，这是量化的学习增量，这意味着形态的能力，以促进学习。
英文摘要：
When controllers (brains) and morphologies (bodies) of robots simultaneously evolve, this can lead to a problem, namely the brain & body mismatch problem. In this research, we propose a solution of lifetime learning. We set up a system where modular robots can create offspring that inherit the bodies of parents by recombination and mutation. With regards to the brains of the offspring, we use two methods to create them. The first one entails solely evolution which means the brain of a robot child is inherited from its parents. The second approach is evolution plus learning which means the brain of a child is inherited as well, but additionally is developed by a learning algorithm - RevDEknn. We compare these two methods by running experiments in a simulator called Revolve and use efficiency, efficacy, and the morphology intelligence of the robots for the comparison. The experiments show that the evolution plus learning method does not only lead to a higher fitness level, but also to more morphologically evolving robots. This constitutes a quantitative demonstration that changes in the brain can induce changes in the body, leading to the concept of morphological intelligence, which is quantified by the learning delta, meaning the ability of a morphology to facilitate learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
