from: http://citeseer.ist.psu.edu/correct/67867

This paper explores the application of genetic algorithms to the learning of local robot navigation behaviors for reactive control systems. Our approach evolves reactive control systems in various environments, thus creating sets of "ecological niches" that can be used in similar environments. The use of genetic algorithms as an unsupervised learning method for a reactive control architecture greatly reduces the effort required to configure a navigation system. Unlike standard genetic...

From: http://citeseer.ist.psu.edu/schultz94evolving.html

This paper discusses the use of evolutionary computation to evolve behaviors that exhibit emergent intelligent behavior. Genetic algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the actual robot. Some of the emergent behavior is described in detail. INTRODUCTION The field of robotics offers an endless supply of difficult problems, requiring an equally impressive...

Abstract: Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots.

Abstract: this paper, we use the genetic programming paradigm to evolve a computer program that exhibits emergent behavior and enables an autonomous mobile robot to follow the walls of an irregularly shaped room. The evolutionary process is driven only by the fitness of the programs in solving the problem