  The food packaging industry handles an immense variety of food products with
wide-ranging shapes and sizes, even within one kind of food. Menus are also
diverse and change frequently, making automation of pick-and-place difficult. A
popular approach to bin-picking is to first identify each piece of food in the
tray by using an instance segmentation method. However, human annotations to
train these methods are unreliable and error-prone since foods are packed close
together with unclear boundaries and visual similarity making separation of
pieces difficult. To address this problem, we propose a method that trains
purely on synthetic data and successfully transfers to the real world using
sim2real methods by creating datasets of filled food trays using high-quality
3d models of real pieces of food for the training instance segmentation models.
Another concern is that foods are easily damaged during grasping. We address
this by introducing two additional methods -- a novel adaptive finger mechanism
to passively retract when a collision occurs, and a method to filter grasps
that are likely to cause damage to neighbouring pieces of food during a grasp.
We demonstrate the effectiveness of the proposed method on several kinds of
real foods.
