Optimal Control and Reinforcement Learning for Robot: A Survey

Haodong Feng, Lei Yu, Yuqing Chen*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


Along with the development of systems and their applications, conventional control approaches are limited by system complexity and functions. The development of reinforcement learning and optimal control has become an impetus of engineering, which has show large potentials on automation. Currently, the optimization applications on robot are facing challenges caused by model bias, high dimensional systems, and computational complexity. To solve these issues, several researches proposed available data-driven optimization approaches. This survey aims to review the achievements on optimal control and reinforcement learning approaches for robots. This is not a complete and exhaustive survey, but provides some latest and remarkable achievements for optimal control of robots. It introduces the background and facing problem statement at the beginning. The developments of the solutions to existed issues for robot control and some notable control methods in these areas are reviewed briefly. In addition, the survey discusses the future development prospects from four aspects as research directions to achieve improving the efficiency of control, the artificial assistant learning, the applications in extreme environment and related subjects. The interdisciplinary researches are essential for engineering fields based on optimal control methods according to the perspective; which would not only promote engineering equipment to be more intelligent, but extend applications of optimal control approaches.

Original languageEnglish
Title of host publicationCollaborative Computing
Subtitle of host publicationNetworking, Applications and Worksharing - 17th EAI International Conference, CollaborateCom 2021, Proceedings
EditorsHonghao Gao, Xinheng Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030926342
Publication statusPublished - 2021
Event17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021 - Virtual, Online
Duration: 16 Oct 202118 Oct 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume406 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X


Conference17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021
CityVirtual, Online


  • Data-driven optimization
  • Model bias
  • Optimal control
  • Reinforcement learning

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