  When transferring a Deep Reinforcement Learning model from simulation to the
real world, the performance could be unsatisfactory since the simulation cannot
imitate the real world well in many circumstances. This results in a long
period of fine-tuning in the real world. This paper proposes a self-supervised
vision-based DRL method that allows robots to pick and place objects
effectively and efficiently when directly transferring a training model from
simulation to the real world. A height-sensitive action policy is specially
designed for the proposed method to deal with crowded and stacked objects in
challenging environments. The training model with the proposed approach can be
applied directly to a real suction task without any fine-tuning from the real
world while maintaining a high suction success rate. It is also validated that
our model can be deployed to suction novel objects in a real experiment with a
suction success rate of 90\% without any real-world fine-tuning. The
experimental video is available at: https://youtu.be/jSTC-EGsoFA.
