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

Overview of attention for article published in Frontiers in Robotics and AI, October 2022
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1 X user

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14 Mendeley
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Title
Self-organized Learning from Synthetic and Real-World Data for a Humanoid Exercise Robot
Published in
Frontiers in Robotics and AI, October 2022
DOI 10.3389/frobt.2022.669719
Pubmed ID
Authors

Nicolas Duczek, Matthias Kerzel, Philipp Allgeuer, Stefan Wermter

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 4 29%
Student > Ph. D. Student 3 21%
Unknown 7 50%
Readers by discipline Count As %
Unspecified 4 29%
Neuroscience 3 21%
Social Sciences 1 7%
Unknown 6 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 October 2022.
All research outputs
#18,958,378
of 23,493,900 outputs
Outputs from Frontiers in Robotics and AI
#1,343
of 1,559 outputs
Outputs of similar age
#306,940
of 442,160 outputs
Outputs of similar age from Frontiers in Robotics and AI
#77
of 109 outputs
Altmetric has tracked 23,493,900 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,559 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,160 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.