Using Self-Organizing Distinctive State Abstraction to Navigate a Maze World

Abstract

This paper presents a developmental robotics experiment implementing self-organizing distinctive state abstraction and growing neural gas. The robot brain takes sensory input of eight sonars, a light sensor, and a stall sensor and has motor outputs of translation and rotation. The robot is placed in a maze world with a goal signified by a light source. We test whether the robot can learn to traverse the maze from increasingly distant starting points, using reinforcement learning. Although the robot does not learn to traverse the maze, it seems likely that an adaptation of our algorithm could complete this task.