Human-Assisted Learning of Object Models through Active Object Exploration

Abstract

As robots are increasingly acting in real-world environments, learning and recognition of objects is a problem. Existing methods for learning visual object models use offline techniques to generate high-quality models or online techniques to dynamically expand the object model library. We present an online learning method that creates visual object models through active object exploration. Our approach enables a robot to use manipulations of an object to learn autonomously visual features from several points of view. The ability to segment background, robot parts and the object in the visual space allows to filter irrelevant feature points. This improves the quality of the object model while decreasing its size. Finally, a human-robot interaction enables a human collaborator to improve the object model. The method is evaluated on a Pepper robot, showing the improvement in performance and accuracy with respect to interactive learning.