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Review
. 2021 Feb 11;21(4):1278.
doi: 10.3390/s21041278.

Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning

Jiang Hua  1 , Liangcai Zeng  1 , Gongfa Li  1 , Zhaojie Ju  2
Affiliations
Review

Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning

Jiang Hua et al. Sensors (Basel). .

Abstract

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.

Keywords: adaptive and robust control; deep reinforcement learning; dexterous manipulation; imitation learning; transfer learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of the structure of the survey.
Figure 2
Figure 2
The classification of reinforcement learning.
Figure 3
Figure 3
Classification of imitation learning.
Figure 4
Figure 4
Principle of transfer learning for robot manipulation.
See this image and copyright information in PMC

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