Evolutionary robotics-a children's game

Abstract

The authors explore the concept of development without programming by children. Especially, they look at the case of developing robot control systems. The evolutionary robotics approach has shown that in some cases, given a mathematically described fitness function, it is possible to achieve an automatic development of robot controllers. However, it is questionable how one is to construct the mathematical fitness function. So they applied an interactive genetic algorithm to the problem of developing robot controllers and achieved and evolutionary robotics approach that allows children without any programming knowledge to develop controller for LEGO robots. They used neural networks as robot controllers, and found that combining the interactive genetic algorithm with a kind of reinforcement learning-development at the evolutionary time scale combined with life-time development-reduces the development time drastically. Hence, they overcome one of the major drawbacks of the interactive genetic algorithm, namely the development time.