Segmentation and learning of unknown objects through physical interaction

David Schiebener, Ales Ude, Jun Morimoto, Tamim Asfour, Rüdiger Dillmann

Abstract: This paper reports on a new approach for segmentation and learning of new, unknown objects with a humanoid robot. No prior knowledge about the objects or the environment is needed. The only necessary assumptions are firstly, that the object has a (partly) smooth surface that contains some distinctive visual features and secondly, that the object moves as a rigid body. The robot uses both its visual and manipulative capabilities to segment and learn unknown objects in unknown environments. The segmentation algorithm is based on pushing hypothetical objects by the robot, which provides a sufficient amount of information to distinguish the object from the background. In the case of a successful segmentation, additional features are associated with the object over several pushing-and-verification iterations. The accumulated features are used to learn the appearance of the object from multiple viewing directions. We show that the learned model, in combination with the proposed segmentation process, allows robust object recognition in cluttered scenes.