Title: A Robotic Auto-Focus System based on Deep Reinforcement Learning,
Abstract: Considering its advantages in dealing with high-dimensional visual input and
learning control policies in discrete domain, Deep Q Network (DQN) could be an
alternative method of traditional auto-focus means in the future. In this
paper, based on Deep Reinforcement Learning, we propose an end-to-end approach
that can learn auto-focus policies from visual input and finish at a clear spot
automatically. We demonstrate that our method - discretizing the action space
with coarse to fine steps and applying DQN is not only a solution to auto-focus
but also a general approach towards vision-based control problems. Separate
phases of training in virtual and real environments are applied to obtain an
effective model. Virtual experiments, which are carried out after the virtual
training phase, indicates that our method could achieve 100% accuracy on a
certain view with different focus range. Further training on real robots could
eliminate the deviation between the simulator and real scenario, leading to
reliable performances in real applications.