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136 Biomimetics: Biologically Inspired Technologies

product are engineered in concert rather than sequentially. Some small changes to the morphology
may make the controller design task much simpler and vice versa.

4.3.3 Evolving Bodies and Brains

One may wonder what happens if the evolutionary process is given even more freedom in the
design of both the morphology and control. Sims (1994) explored this idea in simulation using 3D
cubes and oscillators as building blocks. Inspired by that work, we were interested in exploring
physically realizable machines and start with lower-level building blocks, such as simple neurons
and 1D elements (Lipson and Pollack, 2000). We used a design space consisting of bars and linear
actuators for the morphology and neurons for the control (Figure 4.4a). The design space we used
comprised bars and actuators as building blocks of structure and artificial neurons as building
blocks of control. Bars connected with free joints can potentially form trusses that represent
arbitrary rigid, flexible, and articulated structures, as well as multiple detached structures, and
emulate revolute, linear, and planar joints at various levels of hierarchy. Similarly, sigmoidal
neurons can connect to create arbitrary control architectures such as feed-forward and recurrent
nets, state machines and multiple independent controllers. The bars can connect to each other
through ball-and-socket joints, neurons can connect to other neurons through synaptic connections,
and neurons can connect to bars. In the latter case, the length of the bar is governed by the output
of the neuron by means of a linear actuator. No sensors were used. Variation operators used in the
evolutionary process were allowed to connect, disconnect, add, remove, or modify any of the
components.
Starting with a population of 200 blank machines that were comprised initially of zero bars
and zero neurons, we conducted evolution in simulation. The fitness of a machine was determined
by its locomotion ability: the net distance its center of mass moved on an infinite plane in a fixed
duration. The process iteratively selected fitter machines, created offspring by adding, modifying,
and removing building blocks and replaced them into the population. This process typically
continued for 300 to 600 generations. Both body (morphology) and brain (control) were thus
coevolved simultaneously. The simulator we used for evaluating fitness supported quasi-static
motion in which each frame is statically stable. This kind of motion is simpler to transfer reliably
into reality, yet is rich enough to support low-momentum locomotion.
Typically, several tens of generations passed before the first movement occurred. For example,
at a minimum, a neural network generating varying output must assemble and connect to an
actuator for any motion at all (see sequence in Figure 4.4a, for an example). A sample instance
of an entire generation, thinned down to unique individuals is shown in Figure 4.4b. Various
patterns of evolutionary dynamics emerged, some of which are reminiscent of natural phylogenic
trees. Figure 4.4c presents examples of extreme cases of convergence, speciation, and massive
extinction, and Figure 4.4d shows progress over time of one evolutionary run. Figure 4.4e shows
some of the fitter machines that emerged from this process; these machines were ‘‘copied’’ from
simulation into reality using rapid-prototyping technology (Figure 4.4f). The machines performed
in reality, showing the first instance of a physical robot whose entire design — both morphology
and control — were evolved.
In spite of the relatively simple task and environment (locomotion over an infinite horizontal
plane), surprisingly different and elaborate solutions were evolved. Machines typically contained
around 20 building blocks, sometimes with significant redundancy (perhaps to make mutation less
likely to be catastrophic). Not less surprising was the fact that some exhibited symmetry, which was
neither specified nor rewarded for anywhere in the code; a possible explanation is that symmetric
machines are more likely to move in a straight line, consequently covering a greater net distance
and acquiring more fitness. Similarly, successful designs appear to be robust in the sense that
changes to bar lengths would not significantly hamper their mobility. The three samples shown in
Figure 4.4d exploit principles of ratcheting, anti-phase synchronization, and dragging. Others (not