  Automating warehouse operations can reduce logistics overhead costs,
ultimately driving down the final price for consumers, increasing the speed of
delivery, and enhancing the resiliency to market fluctuations.
  This extended abstract showcases a large-scale package manipulation from
unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which is
used for picking and singulating up to 6 million packages per day and so far
has manipulated over 2 billion packages. It describes the various heuristic
methods developed over time and their successor, which utilizes a pick success
predictor trained on real production data.
  To the best of the authors' knowledge, this work is the first large-scale
deployment of learned pick quality estimation methods in a real production
system.
