  Deep learning techniques have been widely applied, achieving state-of-the-art
results in various fields of study. This survey focuses on deep learning
solutions that target learning control policies for robotics applications. We
carry out our discussions on the two main paradigms for learning control with
deep networks: deep reinforcement learning and imitation learning. For deep
reinforcement learning (DRL), we begin from traditional reinforcement learning
algorithms, showing how they are extended to the deep context and effective
mechanisms that could be added on top of the DRL algorithms. We then introduce
representative works that utilize DRL to solve navigation and manipulation
tasks in robotics. We continue our discussion on methods addressing the
challenge of the reality gap for transferring DRL policies trained in
simulation to real-world scenarios, and summarize robotics simulation platforms
for conducting DRL research. For imitation leaning, we go through its three
main categories, behavior cloning, inverse reinforcement learning and
generative adversarial imitation learning, by introducing their formulations
and their corresponding robotics applications. Finally, we discuss the open
challenges and research frontiers.
