Growing simulated robots with environmental feedback: an eco-evo-devo approach

Robots are still missing the ability to adapt to new environments. However, biological systems are able to adapt to new environments with ease; perhaps because they have the ability to react to environmental input during a growth phase with changes not only in behaviour, but also morphology. Yet within the field of robots, environmental based development of morphology is an under researched area. In this paper we use an evolutionary algorithm to evolve neural cellular automata capable of inducing environmental based developmental plasticity in robots. We use the kinetic energy of each cell and its neighbours as an input to our network, the output of which determines the position of new cell growth. We evolve our neural cellular automata first in three individual environments and then also for performance in multiple environments. We show that the networks that use environmental feedback outperform those that do not and that by introducing environmental feedback during development, more adaptive and better performing robots are potentially possible.