  Most existing 6-DoF robot grasping solutions depend on strong supervision on
grasp pose to ensure satisfactory performance, which could be laborious and
impractical when the robot works in some restricted area. To this end, we
propose a self-supervised 6-DoF grasp pose detection framework via an Augmented
Reality (AR) teleoperation system that can efficiently learn human
demonstrations and provide 6-DoF grasp poses without grasp pose annotations.
Specifically, the system collects the human demonstration from the AR
environment and contrastively learns the grasping strategy from the
demonstration. For the real-world experiment, the proposed system leads to
satisfactory grasping abilities and learning to grasp unknown objects within
three demonstrations.
