Cellular encoding for interactive evolutionary robotics

This work reports experiments in interactive evolutionary robotics. The goal is to evolve an Artificial Neural Network (ANN) to control the locomotion of an 8-legged robot. The ANNs are encoded using a cellular developmental process called cellular encoding. In a previous work similar experiments have been carried on successfully on a simulated robot. They took however around 1,000,000 different ANN evaluations. In this work the fitness is determined on a real robot, and no more than a few hundreds evaluations can be performed. Various ideas were implemented so as to decrease the required number of evaluations from 1,000,000 to 200. First we used cell cloning and link typing. Second we did as many things as possible interactively: interactive problem decomposition, interactive syntactic constraints, interactive fitness. More precisely: 1- A modular design was chosen where a controller for an individual leg, with a precise neuronal interface was developed. 2- Syntactic constraints were used to promoting useful building blocs and impose an 8-fold symmetry. 3- We determine the fitness interactively by hand. We can reward features that would otherwise be very difficult to locate automatically. Interactive evolutionary robotics turns out to be quite successful, in the first bug-free run a global locomotion controller that is faster than a programmed controller could be evolved.