  To be effective in unstructured and changing environments, robots must learn
to recognize new objects. Deep learning has enabled rapid progress for object
detection and segmentation in computer vision; however, this progress comes at
the price of human annotators labeling many training examples. This paper
addresses the problem of extending learning-based segmentation methods to
robotics applications where annotated training data is not available. Our
method enables pixelwise segmentation of grasped objects. We factor the problem
of segmenting the object from the background into two sub-problems: (1)
segmenting the robot manipulator and object from the background and (2)
segmenting the object from the manipulator. We propose a kinematics-based
foreground segmentation technique to solve (1). To solve (2), we train a
self-recognition network that segments the robot manipulator. We train this
network without human supervision, leveraging our foreground segmentation
technique from (1) to label a training set of images containing the robot
manipulator without a grasped object. We demonstrate experimentally that our
method outperforms state-of-the-art adaptable in-hand object segmentation. We
also show that a training set composed of automatically labelled images of
grasped objects improves segmentation performance on a test set of images of
the same objects in the environment.
