  One of the most efficient ways for a learning-based robotic arm to learn to
process complex tasks as human, is to directly learn from observing how human
complete those tasks, and then imitate. Our idea is based on success of Deep
Q-Learning (DQN) algorithm according to reinforcement learning, and then extend
to Deep Deterministic Policy Gradient (DDPG) algorithm. We developed a
learning-based method, combining modified DDPG and visual imitation network.
Our approach acquires frames only from a monocular camera, and no need to
either construct a 3D environment or generate actual points. The result we
expected during training, was that robot would be able to move as almost the
same as how human hands did.
