Robot Control Optimization Using Reinforcement Learning
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 ne...
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| Vydané v: | Journal of intelligent & robotic systems Ročník 21; číslo 3; s. 221 - 238 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Dordrecht
Kluwer
01.03.1998
Springer Nature B.V |
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| ISSN: | 0921-0296, 1573-0409 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Sun, Wen-Yu Song, Kai-Tai |
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| Cites_doi | 10.1109/37.257890 10.1109/ICSMC.1991.169893 10.1109/TSMC.1974.5408453 10.1109/TSMC.1985.6313371 10.1109/70.88112 10.7551/mitpress/5236.001.0001 10.1016/0893-6080(88)90007-X 10.1016/0893-6080(90)90056-Q 10.1115/1.3139652 |
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| Keywords | Learning Experimental result Simulation Reinforcement learning Control synthesis Tracking task Neural network Trajectory Artificial intelligence Robotics |
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| References | 150354_CR16 K. S. Narendra (150354_CR10) 1974; 14 R. S. Sutton (150354_CR15) 1988; 3 150354_CR13 150354_CR6 150354_CR14 B. Widrow (150354_CR17) 1985 A. G. Barto (150354_CR4) 1985; 15 V. Gullapalli (150354_CR5) 1994; 14 D. E. Rumelhart (150354_CR12) 1986 M. H. Raibert (150354_CR11) 1981; 102 150354_CR8 W. T. Miller (150354_CR9) 1990; 6 J. S. Albus (150354_CR2) 1975; 97 C. H. An (150354_CR1) 1988 A. G. Barto (150354_CR3) 1990 V. Gullapalli (150354_CR7) 1990; 3 |
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| SubjectTerms | Applied sciences Artificial intelligence Artificial neural networks Computer science; control theory; systems Computer simulation Connectionism. Neural networks Control theory. Systems Dynamic models Exact sciences and technology Learning Learning theory Machine learning Mathematical models Neural networks Reinforcement Robot arms Robot control Robot dynamics Robot learning Robotics Robots Studies Tracking Tracking control |
| Title | Robot Control Optimization Using Reinforcement Learning |
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