  Object recognition and instance segmentation are fundamental skills in any
robotic or autonomous system. Existing state-of-the-art methods are often
unable to capture meaningful uncertainty in challenging or ambiguous scenes,
and as such can cause critical errors in high-performance applications. In this
paper, we explore a class of distributional instance segmentation models using
latent codes that can model uncertainty over plausible hypotheses of object
masks. For robotic picking applications, we propose a confidence mask method to
achieve the high precision necessary in industrial use cases. We show that our
method can significantly reduce critical errors in robotic systems, including
our newly released dataset of ambiguous scenes in a robotic application. On a
real-world apparel-picking robot, our method significantly reduces double pick
errors while maintaining high performance.
