Collective Learning of Concepts Using a Robot Team

Abstract

Autonomous learning of objects using visual information is important to robotics as it can be used for local and global localization problems, and for service tasks such as searching for objects in unknown places. In a robot team, the learning process can be distributed among robots to reduce training time and produce more accurate models. This paper introduces a new learning framework where individual representations of objects are learned on-line by a robot team while traversing an environment without prior knowledge on the number or nature of the objects to learn. Individual concepts are shared among robots to improve their own concepts, combining information from other robots that saw the same object, and to acquire a new representation of an object not seen by the robot. Since the robots do not know in advance how many objects they will encounter, they need to decide whether they are seeing a new object or a known object. Objects are characterized by local and global features and a Bayesian approach is used to combine them, and to recognize objects. We empirically evaluated our approach with a real world robot team with very promising results.