A stable method for task priority adaptation in quadratic programming via reinforcement learning

In emerging manufacturing facilities, robots must enhance their flexibility. They are expected to perform complex jobs, showing different behaviors on the need, all within unstructured environments, and without requiring reprogramming or setup adjustments. To address this challenge, we introduce the...

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Veröffentlicht in:	Robotics and computer-integrated manufacturing Jg. 91; S. 102857
Hauptverfasser:	Testa, Andrea, Laghi, Marco, Bianco, Edoardo Del, Raiola, Gennaro, Hoffman, Enrico Mingo, Ajoudani, Arash
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2025 Elsevier
Schlagworte:	Computer Science Machine Learning Machine learning for robot control Optimization and optimal control Reinforcement learning Robotics Machine learning for robot control Optimization and optimal control Reinforcement learning Reinforcement Learning Machine Learning for Robot Control Optimization and Optimal Control Optimization and Optimal Control Reinforcement Learning Machine Learning for Robot Control
ISSN:	0736-5845, 1879-2537
Online-Zugang:	Volltext
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