Abstract

Autonomous mobile robots need good models of their environment, sensors and actuators to navigate reliably and efficiently. While this information can be supplied by humans, or learned from scratch through active exploration, such approaches are tedious and time-consuming. Our approach is to provide the robot with the topological and geometrical constraints that are easily obtainable by humans, and have the robot learn the rest while in the course of performing its tasks. We present GROW-BW, an unsupervised and passive distance learning algorithm that overcomes the problem that the robot can never be sure about its location if it is not allowed to reduce its uncertainty by asking a teacher or executing localization actions. Advantages of GROW-BW include that the robot can be used immediately to perform navigation tasks and improves its performance over time, focusing its attention to routes that are more relevant for its tasks. We demonstrate that GROW-BW can learn good distance, sensor, and actuator models with only a small amount of experience.

Citations