The Effects of Learning in Morphologically Evolving Robot Systems

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 has been proposed relatively long ago by the so-called Triangle of Life approach. 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, we demonstrate that the evolved morphologies will be also different, even though learning only directly affects the controllers. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the learning delta defined as the performance difference between the inherited and the learned brain, and find that it is growing throughout the evolutionary process. This shows that evolution produces robots with an increasing plasticity, that is, consecutive generations become better learners and, consequently, they perform better at the given task. Moreover, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system methodology with practical benefits.

Automated summary

This paper investigates the effects of an infant learning mechanism on the evolution of robot morphologies and controllers. The results show that learning can significantly improve task performance and decrease the number of generations required to reach a certain fitness level. Additionally, the study finds that even though learning only directly affects the controllers, the evolved morphologies also differ. This demonstrates that changes in the brain can induce changes in the body. Furthermore, the research shows that evolution produces robots with increasing plasticity, indicating consecutive generations become better learners and performers.