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Simultaneous learning of hierarchy and primitives for complex robot tasks

Overview of attention for article published in Autonomous Robots, April 2018
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Mentioned by

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1 X user

Citations

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16 Dimensions

Readers on

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54 Mendeley
Title
Simultaneous learning of hierarchy and primitives for complex robot tasks
Published in
Autonomous Robots, April 2018
DOI 10.1007/s10514-018-9749-y
Authors

Anahita Mohseni-Kabir, Changshuo Li, Victoria Wu, Daniel Miller, Benjamin Hylak, Sonia Chernova, Dmitry Berenson, Candace Sidner, Charles Rich

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

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 37%
Student > Master 8 15%
Researcher 4 7%
Professor > Associate Professor 3 6%
Student > Doctoral Student 1 2%
Other 2 4%
Unknown 16 30%
Readers by discipline Count As %
Engineering 21 39%
Computer Science 15 28%
Neuroscience 1 2%
Social Sciences 1 2%
Unknown 16 30%
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 01 May 2018.
All research outputs
#15,823,206
of 23,506,079 outputs
Outputs from Autonomous Robots
#383
of 532 outputs
Outputs of similar age
#208,980
of 326,513 outputs
Outputs of similar age from Autonomous Robots
#7
of 8 outputs
Altmetric has tracked 23,506,079 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 532 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 22nd percentile – i.e., 22% 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 326,513 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.