Evaluating Task Optimization and Reinforcement Learning Models in Robotic Task Parameterization

The rapid evolution of industrial robot hardware has created a technological gap with software, limiting its adoption. The software solutions proposed in recent years have yet to meet the industrial sector's requirements, as they focus more on the definition of task structure than the definitio...

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Veröffentlicht in:	IEEE access Jg. 12; S. 173734 - 173748
Hauptverfasser:	Delledonne, Michele, Villagrossi, Enrico, Beschi, Manuel, Rastegarpanah, Alireza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial intelligence Efficiency Flexibility Industrial robots intuitive robot programming Libraries Machine learning Mathematical models Modular structures Optimization Parameterization Parameters Programming Reinforcement learning Robot learning Robot sensing systems robotic task optimization Robots Service robots Software task-oriented programming
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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