Online learning for object identification by a mobile robot

Abstract

Object identification for a situated robot is a first step towards many relevant behaviours such as human-robot communication, object tracking, object detection, etc. However, the dynamic and unpredictable nature of the world makes it very difficult to design such algorithms. Our goal is to endow a PIONEER 2DX autonomous mobile robot with the ability to learn how to identify objects from its environment, and to maintain this ability through time. In order to do so, we propose an architecture that continuously looks for relevant visual invariant properties related to target objects thanks to online learning techniques.