  To realize a robust robotic grasping system for unknown objects in an
unstructured environment, large amounts of grasp data and 3D model data for the
object are required, the sizes of which directly affect the rate of successful
grasps. To reduce the time cost of data acquisition and labeling and increase
the rate of successful grasps, we developed a self-supervised learning
mechanism to control grasp tasks performed by manipulators. First, a
manipulator automatically collects the point cloud for the objects from
multiple perspectives to increase the efficiency of data acquisition. The
complete point cloud for the objects is obtained by utilizing the hand-eye
vision of the manipulator, and the TSDF algorithm. Then, the point cloud data
for the objects is used to generate a series of six-degrees-of-freedom grasp
poses, and the force-closure decision algorithm is used to add the grasp
quality label to each grasp pose to realize the automatic labeling of grasp
data. Finally, the point cloud in the gripper closing area corresponding to
each grasp pose is obtained; it is then used to train the grasp-quality
classification model for the manipulator. The results of data acquisition
experiments demonstrate that the proposed method allows high-quality data to be
obtained. The simulated results prove the effectiveness of the proposed
grasp-data acquisition method. The results of performing actual grasping
experiments demonstrate that the proposed self-supervised learning method can
increase the rate of successful grasps for the manipulator.
