  Robotic systems in manufacturing applications commonly assume known object
geometry and appearance. This simplifies the task for the 3D perception
algorithms and allows the manipulation to be more deterministic. However, those
approaches are not easily transferable to the agricultural and food domains due
to the variability and deformability of natural food. We demonstrate an
approach applied to poultry products that allows picking up a whole chicken
from an unordered bin using a suction cup gripper, estimating its pose using a
Deep Learning approach, and placing it in a canonical orientation where it can
be further processed. Our robotic system was experimentally evaluated and is
able to generalize to object variations and achieves high accuracy on bin
picking and pose estimation tasks in a real-world environment.
