Reinforcement learning algorithm for industrial robot programming by demonstration

Programming by demonstration represent a significant subject in the field of robotics and it is developing more and more in the direction of robots for services and humanoid robots. Programming by demonstration is much less researched, when we talk about industrial robots. One of the reasons is that...

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Veröffentlicht in:2012 13th International Conference on Optimization of Electrical and Electronic Equipment S. 1517 - 1524
Hauptverfasser: Stoica, M., Sisak, F., Morosan, A. D.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2012
Schlagworte:
Classification algorithms
Learning
Programming
Robot kinematics
Service robots
Software
ISBN:9781467316507, 1467316504
ISSN:1842-0133
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Zusammenfassung:Programming by demonstration represent a significant subject in the field of robotics and it is developing more and more in the direction of robots for services and humanoid robots. Programming by demonstration is much less researched, when we talk about industrial robots. One of the reasons is that an industrial robot has to act in a precise and certain manner. However, extending research regarding programming by demonstration in industrial robots area, could lead to development of intelligent systems, where the industrial robot could be programmed in an easier way. In this paper we proposed an algorithm based on reinforcement learning and we developing, implementing and testing this algorithm which can offer flexibility in intelligent systems. Initially, we have focused our research on the creation of a reasoning algorithm based on artificial neural networks, but the results of this algorithm weren't satisfying, so we have switched our focus towards proposed algorithm. The results of this algorithm is that the robot will be capable to learn from its mistakes and he will know how to act in unknown situation; this will be possible because the robot will get marks for each possible action and he will updates its behavior.
ISBN:9781467316507
1467316504
ISSN:1842-0133
DOI:10.1109/OPTIM.2012.6231926