  This paper proposes a novel method to refine the 6D pose estimation inferred
by an instance-level deep neural network which processes a single RGB image and
that has been trained on synthetic images only. The proposed optimization
algorithm usefully exploits the depth measurement of a standard RGB-D camera to
estimate the dimensions of the considered object, even though the network is
trained on a single CAD model of the same object with given dimensions. The
improved accuracy in the pose estimation allows a robot to grasp apples of
various types and significantly different dimensions successfully; this was not
possible using the standard pose estimation algorithm, except for the fruits
with dimensions very close to those of the CAD drawing used in the training
process. Grasping fresh fruits without damaging each item also demands a
suitable grasp force control. A parallel gripper equipped with special
force/tactile sensors is thus adopted to achieve safe grasps with the minimum
force necessary to lift the fruits without any slippage and any deformation at
the same time, with no knowledge of their weight.
