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
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Zusammenfassung: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 definition and tuning of its execution parameters. A framework for task parameter optimization was developed to address this gap. It breaks down the task using a modular structure, allowing the task optimization piece by piece. The optimization is performed with a dedicated hill-climbing algorithm. This paper revisits the framework by proposing an alternative approach that replaces the algorithmic component with reinforcement learning (RL) models. Five RL models are proposed with increasing complexity and efficiency. A comparative analysis of the traditional algorithm and RL models is presented, highlighting efficiency, flexibility, and usability. The results demonstrate that although RL models improve task optimization efficiency by 95%, they still need more flexibility. However, the nature of these models provides significant opportunities for future advancements.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3504354