Learning Visual Feature Detectors for Obstacle Avoidance using Genetic Programming

Abstract

In this paper, we describe the use of Genetic Programming (GP) techniques to learn a visual feature detection for a mobile robot navigation task. We provide experimental results across a number of different environments, each with different characteristics, and draw conclusions about the performance of the learned feature detector. We also explore the utility of seeding the initial population with a previously evolved individual, and discuss the performance of the resulting individuals.