  Instance segmentation with unseen objects is a challenging problem in
unstructured environments. To solve this problem, we propose a robot learning
approach to actively interact with novel objects and collect each object's
training label for further fine-tuning to improve the segmentation model
performance, while avoiding the time-consuming process of manually labeling a
dataset. The Singulation-and-Grasping (SaG) policy is trained through
end-to-end reinforcement learning. Given a cluttered pile of objects, our
approach chooses pushing and grasping motions to break the clutter and conducts
object-agnostic grasping for which the SaG policy takes as input the visual
observations and imperfect segmentation. We decompose the problem into three
subtasks: (1) the object singulation subtask aims to separate the objects from
each other, which creates more space that alleviates the difficulty of (2) the
collision-free grasping subtask; (3) the mask generation subtask to obtain the
self-labeled ground truth masks by using an optical flow-based binary
classifier and motion cue post-processing for transfer learning. Our system
achieves 70% singulation success rate in simulated cluttered scenes. The
interactive segmentation of our system achieves 87.8%, 73.9%, and 69.3% average
precision for toy blocks, YCB objects in simulation and real-world novel
objects, respectively, which outperforms several baselines.
