The application of Deep Learning in the field of Robotics

Abstract. As a new field with rapid development in the past two decades, deep learning has received more and more attention from researchers. Neural network adopts widely interconnected structure and effective learning mechanisms to simulate the process of information processing in the human brain, which is an important method in the development of artificial intelligence. Compared with shallow models, deep learning can achieve stronger unsupervised autonomous learning capabilities by increasing the number of layers of the network. In the past, the robots needed to be controlled by the humans; they controlled the robot to finish different tasks. In recent years, the researchers have tried to apply deep learning in the field of robots. Deep neural networks can simulate the complex cognitive laws of the human brain, so many researchers apply deep learning to robots to make robots have the thinking ability of humans. This article will review the cases of combining robot and in-depth learning in recent decades, Summarizing the achievements and problems faced in the three fields of robot vision, trajectory planning and motion control.