  We present a novel framework for self-supervised grasped object segmentation
with a robotic manipulator. Our method successively learns an agnostic
foreground segmentation followed by a distinction between manipulator and
object solely by observing the motion between consecutive RGB frames. In
contrast to previous approaches, we propose a single, end-to-end trainable
architecture which jointly incorporates motion cues and semantic knowledge.
Furthermore, while the motion of the manipulator and the object are substantial
cues for our algorithm, we present means to robustly deal with distraction
objects moving in the background, as well as with completely static scenes. Our
method neither depends on any visual registration of a kinematic robot or 3D
object models, nor on precise hand-eye calibration or any additional sensor
data. By extensive experimental evaluation we demonstrate the superiority of
our framework and provide detailed insights on its capability of dealing with
the aforementioned extreme cases of motion. We also show that training a
semantic segmentation network with the automatically labeled data achieves
results on par with manually annotated training data. Code and pretrained model
are available at https://github.com/DLR-RM/DistinctNet.
