Learning object representations by exploration is of great importance for cognitive robots that need to learn about their environment without external help. In this paper we present sensorimotor processes that enable the robot to observe grasped objects from all relevant viewpoints, which makes it possible to learn viewpoint independent object representations. Taking control of the object allows the robot to focus on relevant parts of the images, thus bypassing potential pitfalls of pure bottom-up attention and segmentation. We propose a systematic method to control a robot in order to achieve a maximum range of motion across the 3-D view sphere. This is done by exploiting the task redundancies typically found on a humanoid arm and by avoiding joint limits of the robot. The proposed method brings the robot into configurations that are appropriate for observing objects. It enables us to acquire a wider range of snapshots without regrasping the object.
['Damir Omrcen', 'Ales Ude', 'Kai Welke', 'Tamim Asfour', 'Rüdiger Dillmann']
Sensorimotor processes for learning object representations