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Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking

Overview of attention for article published in Frontiers in Neurorobotics, June 2018
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Title
Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking
Published in
Frontiers in Neurorobotics, June 2018
DOI 10.3389/fnbot.2018.00029
Pubmed ID
Authors

Melanie Jouaiti, Lancelot Caron, Patrick Hénaff

Abstract

It is well-known that human social interactions generate synchrony phenomena which are often unconscious. If the interaction between individuals is based on rhythmic movements, synchronized and coordinated movements will emerge from the social synchrony. This paper proposes a plausible model of plastic neural controllers that allows the emergence of synchronized movements in physical and rhythmical interactions. The controller is designed with central pattern generators (CPG) based on rhythmic Rowat-Selverston neurons endowed with neuronal and synaptic Hebbian plasticity. To demonstrate the interest of the proposed model, the case of handshaking is considered because it is a very common, both physically and socially, but also, a very complex act in the point of view of robotics, neuroscience and psychology. Plastic CPGs controllers are implemented in the joints of a simulated robotic arm that has to learn the frequency and amplitude of an external force applied to its effector, thus reproducing the act of handshaking with a human. Results show that the neural and synaptic Hebbian plasticity are working together leading to a natural and autonomous synchronization between the arm and the external force even if the frequency is changing during the movement. Moreover, a power consumption analysis shows that, by offering emergence of synchronized and coordinated movements, the plasticity mechanisms lead to a significant decrease in the energy spend by the robot actuators thus generating a more adaptive and natural human/robot handshake.

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Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 23%
Student > Bachelor 4 18%
Student > Ph. D. Student 3 14%
Professor 3 14%
Other 1 5%
Other 2 9%
Unknown 4 18%
Readers by discipline Count As %
Engineering 5 23%
Neuroscience 5 23%
Psychology 2 9%
Mathematics 1 5%
Economics, Econometrics and Finance 1 5%
Other 3 14%
Unknown 5 23%
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 18 June 2018.
All research outputs
#18,637,483
of 23,088,369 outputs
Outputs from Frontiers in Neurorobotics
#590
of 886 outputs
Outputs of similar age
#254,227
of 328,957 outputs
Outputs of similar age from Frontiers in Neurorobotics
#22
of 24 outputs
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We're also able to compare this research output to 24 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.