Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period by the so-called Triangle of Life framework has been proposed relatively long ago. However, an empirical assessment is still lacking to-date. In this paper we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level compared to the purely evolutionary approach. Furthermore, although learning only directly affects the controllers, we demonstrate that the evolved morphologies will be also different. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the concept of morphological intelligence quantified by the ability of a given body to learn. We observe that the learning delta, the performance difference between the inherited and the learned brain, is growing throughout the evolutionary process. This shows that evolution is producing robots with an increasing plasticity, that is, consecutive generations are becoming better and better learners which in turn makes them better and better at the given task. All in all, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system architecture with practical benefits.

翻译：同时不断演变的机器人形态(身体)和控制器(肌肉)可能会造成遗传体与后代大脑之间的不匹配。 为了缓解这一问题, 早在很久以前就提出了所谓的生命三角框架, 但是经验性评估仍然缺乏。 在本文中, 我们从不同角度调查了这种学习机制的影响。 我们利用广泛的模拟表明, 学习可以大大提高任务性能, 并减少与纯进化方法相比, 达到某种健康水平所需的几代人数量。 此外, 尽管学习只直接影响控制者, 我们证明进化的形态也会不同。 这提供了数量性证明, 大脑的变化可以引发身体的变化。 最后, 我们考察了以某个特定机构学习能力量化的形态性智力概念。 我们观察到,学习三角洲, 继承的大脑和学习的大脑之间的性能差异在整个进化过程中不断增长。 这显示, 进化正在产生机器人, 与纯进化方法相比, 不断增长的机器人, 也就是, 后代的变形态将变得更好和更好的学习者, 使得大脑的进化过程更加美好, 并且给人带来进化的进化。