Surprise-Based Learning for Developmental Robotics

摘要

This paper presents a learning algorithm called surprise-based learning (SBL) capable of providing a physical robot the ability to autonomously learn and plan in an unknown environment without any prior knowledge of its actions or their impact on the environment. This is achieved by creating a model of the environment using prediction rules. A prediction rule describes the observations of the environment prior to the execution of an action and the forecasted or predicted observation of the environment after the action. The algorithm learns by investigating "surprises", which are inconsistencies between the predictions and observed outcome. SBL has been successfully demonstrated on a modular robot learning and navigating in a small static environment.