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Positioning of the Robotic Arm Using Different Reinforcement Learning Algorithms

Tymoteusz Lindner*, Andrzej Milecki, and Daniel Wyrwał
International Journal of Control, Automation, and Systems, vol. 19, no. 4, pp.1661-1676, 2021

Abstract : Robots are programmed using either the on-line mode, in which the robot programmer manually controls the movement of the robot indicating individual trajectory points or the off mode, in which the programmer enters the program code with predefined trajectory points. Both methods are not easy to be successfully implemented in practice, which is why the research on the development of self-learning methods can be useful. In this paper, for the robot’s positioning task, the four Reinforcement Learning (RL) algorithms in six combinations are investigated. At first, the basics of these algorithms are described. Then they are used in positioning control of the robot’s arm model and the evaluation of positioning accuracy, motion trajectory, and the number of steps required to achieve the goal is taken into account. The simulation results are recorded. The same tests were repeated in laboratory conditions, in which the Mitsubishi robot was controlled. The simulation results are compared with results obtained in reality. Positive results that have been obtained indicate, that the RL algorithms can be successfully applied for the learning of positioning control of a robot arm.

Keyword : Policy gradient, positioning, reinforcement learning, robots programming.

 
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