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Recent advances in object segmentation have demonstrated that deep neuralnetworks excel at object segmentation for specific classes in color and depthimages. However, their performance is dictated by the number of classes andobjects used for training, thereby hindering generalization to never seenobjects or zero-shot samples. To exacerbate the problem further, objectsegmentation 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 forsegmenting zero-shot samples.  In this paper, we present the first framework to segment unknown objects in acluttered scene by repeatedly 'nudging' at the objects and moving them toobtain additional motion cues at every step using only a monochrome monocularcamera. We call our framework NudgeSeg. These motion cues are used to refinethe segmentation masks. We successfully test our approach to segment novelobjects in various cluttered scenes and provide an extensive study with imageand motion segmentation methods. We show an impressive average detection rateof over 86% on zero-shot objects.

NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction