  Food packing industries typically use seasonal ingredients with immense
variety that factory workers manually pack. For small pieces of food picked by
volume or weight that tend to get entangled, stick or clump together, it is
difficult to predict how intertwined they are from a visual examination, making
it a challenge to grasp the requisite target mass accurately. Workers rely on a
combination of weighing scales and a sequence of complex maneuvers to separate
out the food and reach the target mass. This makes automation of the process a
non-trivial affair. In this study, we propose methods that combines 1)
pre-grasping to reduce the degree of the entanglement, 2) post-grasping to
adjust the grasped mass using a novel gripper mechanism to carefully discard
excess food when the grasped amount is larger than the target mass, and 3)
selecting the grasping point to grasp an amount likely to be reasonably higher
than target grasping mass with confidence. We evaluate the methods on a variety
of foods that entangle, stick and clump, each of which has a different size,
shape, and material properties such as volumetric mass density. We show
significant improvement in grasp accuracy of user-specified target masses using
our proposed methods.
