Mind, Body, World- Foundations of Cognitive Science, 2013a

without feature cues. Their goal was to see whether a standard result—rotational
error—could be produced in an agent that did not employ the geometric module,
and indeed which did not represent arena properties at all. Lund and Miglino’s fitness
function simply measured a robot’s closeness to the goal location. After 30 generations
of evolution, they produced a system that would navigate a robot to the goal
location from any of 8 different starting locations with a 41 percent success rate.
Their robots also produced rotational error, for they incorrectly navigated to the
corner 180° from the goal in another 41 percent of the test trials. These results were
strikingly similar to those observed when rats perform reorientation in featureless
rectangular arenas (e.g., Gallistel, 1990).
Importantly, the control system that was evolved by Lund and Miglino (1998)
was simply a set of weighted connections between proximity detectors and motors,
and not an encoding of arena shape.
The geometrical properties of the environment can be assimilated in the sensorymotor
schema of the robot behavior without any explicit representation. In general,
our work, in contrast with traditional cognitive models, shows how environmental
knowledge can be reached without any form of direct representation. (Lund and
Miglino, 1998, p. 198)
If arena shape is not explicitly represented, then how does the control system developed
by Lund and Miglino (1998) produce reorientation task behaviour? When the
robot is far enough from the arena walls that none of the sensors are detecting an
obstacle, the controller weights are such that the robot moves in a gentle curve to
the left. As a result, it never encounters a short wall when it leaves from any of its
eight starting locations! When a long wall is (inevitably) encountered, the robot
turns left and follows the wall until it stops in a corner. The result is that the robot
will be at either the target location or its rotational equivalent.
The control system evolved by Lund and Miglino (1998) is restricted to rectangular
arenas of a set size. If one of their robots is placed in an arena of even a slightly
different size, its performance suffers (Nolfi, 2002). Nolfi used a much longer evolutionary
process (500 generations), and also placed robots in different sized arenas,
to successfully produce devices that would generate typical results not only in a featureless
rectangular arena, but also in arenas of different dimensions. Again, these
robots did so without representing arena shape or geometry.
Nolfi’s (2002) more general control system worked as follows. His robots would
begin by moving forwards and avoiding walls, which would eventually lead them into
a corner. When facing a corner, signals from the corner’s two walls caused the robot
to first turn to orient itself at an angle of 45° from one of the corner’s walls. Then the
robot would make an additional turn that was either clockwise or counterclockwise,
depending upon whether the sensed wall was to the robot’s left or the right.
Elements of Embodied Cognitive Science 241