Learning for a robot: deep reinforcement learning, imitation learning, transfer learning

Jiang Hua, Liangcai Zeng, Gongfa Li, Zhaojie Ju*

*Corresponding author for this work

Research output: Contribution to journalLiterature reviewpeer-review

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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.

Original languageEnglish
Article number1278
Pages (from-to)1-21
Number of pages21
JournalSensors (Switzerland)
Volume21
Issue number4
DOIs
Publication statusPublished - 11 Feb 2021

Keywords

  • Adaptive and robust control
  • Deep reinforcement learning
  • Dexterous manipulation
  • Imitation learning
  • Transfer learning

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