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Evolutionary Robotics and Open-Ended Design Automation 135

Manual design of a neural controller for a legged machine of this sort is possible, but not
easy. The advantage of design automation here is that a design was found with minimal prior
information on how it should be done. We could now reverse engineer the evolved controller to find
out exactly how it works — like biologists. Should the morphology or the task change, we can have
the process redesign new controllers. The evolutionary architecture described here was rather
simple; many more sophisticated neural controller architectures and evolutionary processes are
being explored, such as the use of plasticity (controllers that can learn after they have been
evolved), controllers that grow, and other types of neurons such as spiking neurons (Nolfi et al.,
1994; Floreano and Urzelai, 2001; Floreano et al., 2001, 2005).

4.3.2 Evolving Controllers and Some Aspects of the Morphology

Design of a robot involves not only the design of controller, but the morphology as well. What
happens if some aspects of the morphological design are also allowed to evolve? For example, Lund
et al. (1997) explored the effect of evolutionary adaptation of physical placement of sensors in a
wheeled robot and showed improved performance. Let us examine this process in context of a
legged machine.
Paul and Bongard (2001) used evolutionary adaptation to evolve designs for a bipedal robot in
simulation, as shown in Figure 4.3a. The machine comprises the bottom half of a walker with six
motors (two at each hip and one in each knee), a touch sensor at each foot and an angle sensor at
each joint. The fitness of a controller was the net distance it could make a machine travel. The
controllers had architecture similar to that shown in Figure 4.1b, with the appropriate number of
inputs and outputs.
Evolving 300 controllers over 300 generations created various controllers that could make the
machine move while keeping it upright. Figure 4.3b shows the maximum fitness per generation for
a number of independent runs. While many did not make much progress, some runs were able to
find good controllers, as evident by the curves with high fitness. More importantly, however, was
that this time the evolutionary process was also allowed to vary the mass distribution of the robot
morphology and that this new freedom allowed it to find good solutions. This may suggest that
evolving a controller for a fixed morphology may be too restrictive, and that better machines might
be found if both the controller and the morphology are allowed to coevolve, as they do in nature.
This lends some credibility to the notion of concurrent engineering, where several aspects of a

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Best Fitness with Fixed Morphology 35 Best Fitness with Fixed Morphology 35
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Figure 4.3 Evolving a controller and some morphology parameters for bipedal locomotion: the morphology of
the machine consists of six motors (four at the hip and two at the knees), six angle sensors, and two touch sensors.
The controller is a recurrent network similar to Figure 4.1b. (a) One of the evolved machines, (b) a comparison of
fitness over generations for the fixed morphology (left) and a variable morphology (right). (From Paul, C., Bongard,
J. C. (2001) The road less traveled: morphology in the optimization of biped robot locomotion, Proceedings of
the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2001), Hawaii, U.S.A. With
permission.)