Learning to navigate from limited sensory input: experiments with the Khepera microrobot

Abstract

The goal of this work is to augment reinforcement learning techniques for autonomous robot navigation with a state space encoding more representative of the actual state of the robot in its environment, than available from direct sensor readings. A second goal is to demonstrate the approach in a real-world setting, using the microrobot Khepera (K-Team, Lausanne, Switzerland). The choice of state representation is one of the most critical factors in the performance of reinforcement learning algorithms. The technique of inferring relative positional information indirectly from sensor readings, through unsupervised learning, is an important novel contribution of this work. As demonstrated in the robot experiments, the technique allows to optimally perform sensor fusion and avoids the need of more elaborate sensors conveying explicit information on position coordinates.