Robot Control Optimization Using Reinforcement Learning

Kai-Tai Song*, Wen Yu Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.

Original languageEnglish
Pages (from-to)221-238
Number of pages18
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number3
StatePublished - Mar 1998


  • Artificial neural network
  • Dynamic control
  • Reinforcement learning
  • Robot control


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