A self-supervised learning-based 6-DOF grasp planning method for manipulator

Understand

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.