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An Adaptive Imitation Learning Framework for Robotic Complex Contact-Rich Insertion Tasks

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

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19 Mendeley
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Title
An Adaptive Imitation Learning Framework for Robotic Complex Contact-Rich Insertion Tasks
Published in
Frontiers in Robotics and AI, January 2022
DOI 10.3389/frobt.2021.777363
Pubmed ID
Authors

Yan Wang, Cristian C. Beltran-Hernandez, Weiwei Wan, Kensuke Harada

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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 16%
Researcher 3 16%
Professor 1 5%
Unspecified 1 5%
Other 1 5%
Other 0 0%
Unknown 10 53%
Readers by discipline Count As %
Engineering 5 26%
Computer Science 3 16%
Unspecified 1 5%
Unknown 10 53%
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 11 January 2022.
All research outputs
#18,449,393
of 22,858,915 outputs
Outputs from Frontiers in Robotics and AI
#1,305
of 1,498 outputs
Outputs of similar age
#361,776
of 503,119 outputs
Outputs of similar age from Frontiers in Robotics and AI
#85
of 95 outputs
Altmetric has tracked 22,858,915 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 1,498 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. 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 503,119 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 95 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.