  We consider the problem of robotic grasping using depth + RGB information
sampling from a real sensor. we design an encoder-decoder neural network to
predict grasp policy in real time. This method can fuse the advantage of depth
image and RGB image at the same time and is robust for grasp and observation
height.We evaluate our method in a physical robotic system and propose an
open-loop algorithm to realize robotic grasp operation. We analyze the result
of experiment from multi-perspective and the result shows that our method is
competitive with the state-of-the-art in grasp performance, real-time and model
size. The video is available in https://youtu.be/Wxw_r5a8qV0
