We report on the design and implementation of an autonomous robot that
performs phototaxis under the control of a simulated neural network.
The mechanical configuration of the robot and its neural network contr
oller are patterned after those believed to produce chemotaxis in the
nematode Caenorhabditis elegans. The network is first optimized to pro
duce phototaxis in a simulated nematode-like robot and then is tested
on a real robot. We find that both the simulated and real robot perfor
m reliably, making nearly identical trajectories for similar environme
nts and similar starting conditions. Furthermore, their performance is
robust to significant perturbations of the robot's locomotion paramet
ers. Finally, we discuss the implicit computational rule that this net
work uses to control phototaxis. This makes the results intuitive and
improves our intuition about control of tactic behavior in two dimensi
ons.