Informed Federated Learning to Train a Robotic Arm Inverse Dynamic Model

Access to real-world data in robotics domains is often challenging due to restrictions on data sharing and limited availability. Although privacy and intellectual property concerns are the main barriers, ensuring data access is crucial for advancing data-driven models. Specifically, machine-learning...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 10; H. 10; S. 11022 - 11029
Hauptverfasser:	Jimenez-Perera, Gabriel, Valencia-Vidal, Brayan, Luque, Niceto R., Ros, Eduardo, Barranco, Francisco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Collaborative robots Data models Data privacy Data sets for robot learning Deep learning methods Distributed databases Dynamic models Federated learning Information sharing Intellectual property Machine learning Privacy Robot arms Robot learning Robotics Robots Servers Training Trajectory
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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