  A human hand can grasp a desired number of objects at once from a pile based
solely on tactile sensing. To do so, a robot needs to grasp within a pile,
sense the number of objects in the grasp before lifting, and predict the number
of objects that will remain in the grasp after lifting. It is a challenging
problem because when making the prediction, the robotic hand is still in the
pile and the objects in the grasp are not observable to vision systems.
Moreover, some objects that are grasped by the hand before lifting from the
pile may fall out of the grasp when the hand is lifted. This occurs because
they were supported by other objects in the pile instead of the fingers of the
hand. Therefore, a robotic hand should sense the number of objects in a grasp
using its tactile sensors before lifting. This paper presents novel
multi-object grasping analyzing methods for solving this problem. They include
a grasp volume calculation, tactile force analysis, and a data-driven deep
learning approach. The methods have been implemented on a Barrett hand and then
evaluated in simulations and a real setup with a robotic system. The evaluation
results conclude that once the Barrett hand grasps multiple objects in the
pile, the data-driven model can predict, before lifting, the number of objects
that will remain in the hand after lifting. The root-mean-square errors for our
approach are 0.74 for balls and 0.58 for cubes in simulations, and 1.06 for
balls, and 1.45 for cubes in the real system.
