  As the number of the robot's degrees of freedom increases, the implementation
of robot motion becomes more complex and difficult. In this study, we focus on
learning 6DOF-grasping motion and consider dividing the grasping motion into
multiple tasks. We propose to combine imitation and reinforcement learning in
order to facilitate a more efficient learning of the desired motion. In order
to collect demonstration data as teacher data for the imitation learning, we
created a virtual reality (VR) interface that allows humans to operate the
robot intuitively. Moreover, by dividing the motion into simpler tasks, we
simplify the design of reward functions for reinforcement learning and show in
our experiments a reduction in the steps required to learn the grasping motion.
