Learning objects on the fly - object recognition for the here and now

We present a robotic vision system for object recognition, pose estimation and fast object learning. Our approach uses the Dynamic Neural Field Theory to combine bottom-up recognition of matching patterns and top-down estimation of pose parameters in a recurrent loop. Because Dynamic Neural Fields provide the system with stabilized percepts that still track changes in the incoming sensory stream, the system is able to do pose tracking even if objects are shortly occluded or distractor objects are moved into the scene.