Self-organized Learning from Synthetic and Real-World Data for a Humanoid Exercise Robot

Self-organized Learning from Synthetic and Real-World Data for a Humanoid Exercise Robot

We propose a neural learning approach for a humanoid exercise robot that can automatically analyze and correct physical exercises. Such an exercise robot should be able to train many different human partners over time and thus requires the ability for lifelong learning. To this end, we develop a mod...

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Bibliographic Details
Main Authors: Nicolas Duczek, Matthias Kerzel, Philipp Allgeuer , Stefan Wermter 
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Robotics and AI
Subjects:
self-organizing networks
physical exercise
human-robot interaction
unsupervised learning
synthetic data
Online Access:https://www.frontiersin.org/articles/10.3389/frobt.2022.669719/full

Internet

https://www.frontiersin.org/articles/10.3389/frobt.2022.669719/full

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