  Humans, this species expert in grasp detection, can grasp objects by taking
into account hand-object positioning information. This work proposes a method
to enable a robot manipulator to learn the same, grasping objects in the most
optimal way according to how the gripper has approached the object. Built on
deep learning, the proposed method consists of two main stages. In order to
generalize the network on unseen objects, the proposed Approach-based Grasping
Inference involves an element decomposition stage to split an object into its
main parts, each with one or more annotated grasps for a particular approach of
the gripper. Subsequently, a grasp detection network utilizes the decomposed
elements by Mask R-CNN and the information on the approach of the gripper in
order to detect the element the gripper has approached and the most optimal
grasp. In order to train the networks, the study introduces a robotic grasping
dataset collected in the Coppeliasim simulation environment. The dataset
involves 10 different objects with annotated element decomposition masks and
grasp rectangles. The proposed method acquires a 90% grasp success rate on seen
objects and 78% on unseen objects in the Coppeliasim simulation environment.
Lastly, simulation-to-reality domain adaptation is performed by applying
transformations on the training set collected in simulation and augmenting the
dataset, which results in a 70% physical grasp success performance using a
Delta parallel robot and a 2 -fingered gripper.
