Evolutionary Robotics and Open-Ended Design Automation - Biomimetics: Biologically Inspired Technologies - page 144

Figure 4.7 (See color insert following page 302) Artificial ontogeny: Growing machines using gene regulatory
networks. (a) An example of cells that can differentiate into structural, passive cells (dark), or active cells (bright)
which contains neurons responsible for sensing (T ¼ touch, A ¼ angle) and motor actuation (M). The connectivity
of the neurons is determined by propagation of ''chemicals'' expressed by genes and sensors, who are themselves
expressed in response to chemicals in a regulatory network. (b) Three machines evolved to be able to push a block.
(c) The distribution of genes responsible for neurogenesis (red) and morphogenesis (blue) shows a clear separ-
ation that suggests an emergence of a ''body'' and a ''brain.'' (From Bongard, J. C., Pfeifer, R. (2003) Evolving
complete agents using artificial ontogeny. In: Hara, F., Pfeifer, R. (eds), Morpho-Functional Machines: the New
Species (Designing Embodied Intelligence), Springer-Verlag, New York, New York. With permission.)

cells (colored blue) are relatively separated, suggesting an initial emergence of what we call
''body'' and ''brain.''

4.5 EVOLVING MACHINES IN PHYSICAL REALITY

Though many robotic experiments are carried out in simulation, a robot must ultimately reside in
physical reality. Applying evolutionary processes to physical machines is difficult for two reasons.
First, even if we are only evolving controllers for a fixed machine, each evaluation of a candidate
controller involves trying it out in reality. This is a slow and costly process that also wears out
the target system. Performing thousand of evaluations is usually impractical. Second, if we are
evolving morphology as well, then how would these morphological changes take place in reality?
Changes to the controller can be done simply by reprogramming, but changes to the morphology
require more sophisticated processes. Nature has some interesting solutions to this problem, such
as growing materials, or self-assembling and self-replicating basic building blocks like cells. Let
us examine these two approaches.

4.5.1 Evolving Controllers for Physical Morphologies

One approach to evolving controllers for fixed morphologies is to make a simulator that is so
perfect, that whatever works in simulation will work in reality equally well. Unfortunately, such a