  Recent advances in object segmentation have demonstrated that deep neural
networks excel at object segmentation for specific classes in color and depth
images. However, their performance is dictated by the number of classes and
objects used for training, thereby hindering generalization to never seen
objects or zero-shot samples. To exacerbate the problem further, object
segmentation using image frames rely on recognition and pattern matching cues.
Instead, we utilize the 'active' nature of a robot and their ability to
'interact' with the environment to induce additional geometric constraints for
segmenting zero-shot samples.
  In this paper, we present the first framework to segment unknown objects in a
cluttered scene by repeatedly 'nudging' at the objects and moving them to
obtain additional motion cues at every step using only a monochrome monocular
camera. We call our framework NudgeSeg. These motion cues are used to refine
the segmentation masks. We successfully test our approach to segment novel
objects in various cluttered scenes and provide an extensive study with image
and motion segmentation methods. We show an impressive average detection rate
of over 86% on zero-shot objects.
