Learning to Synchronously Imitate Gestures Using Entrainment Effect

Ansermin, Eva; Mostafaoui, Ghilès; Beaussé, Nils; Gaussier, Philippe

doi:10.1007/978-3-319-43488-9_20

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…In other recent examples, using a very simple neural network providing minimal sensorimotor adaptation capabilities to the robot, unintentional motor coordination could emerge during an imitation game (of a simple gesture) with a human (Hasnain et al 2012;. An extension of this work demonstrated that a robot could quickly and "online" learn more complex gestures and synchronize its behavior to the human partner based on the same sensorimotor approach (Ansermin et al 2016).…”

Section: Ludovic Marin a And Ghiles Mostafaoui B Amentioning

confidence: 74%

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

Baldassarre¹,

Santucci²,

Cartoni³

et al. 2017

Behav Brain Sci

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Section: Ludovic Marin a And Ghiles Mostafaoui B Amentioning

confidence: 74%

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

Baldassarre¹,

Santucci²,

Cartoni³

et al. 2017

Behav Brain Sci

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“…Closer to this work, [10] introduced a model designed to reproduce rhythmic arm movements with the NAO robot. A reservoir of oscillators provides one with a close frequency and while the oscillator can be slightly entrained during the interaction, the oscillators do not retain the frequency, going back to their original properties right afterwards.…”

Section: Related Workmentioning

confidence: 99%

Motor Coordination Learning for Rhythmic Movements

Jouaiti

Hénaff

2019

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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The perspective of ubiquitous robots raises the issue of social acceptance. It is our belief that a successful robot integration relies on adequate social responses. Human social interactions heavily rely on synchrony which leads humans to connect emotionally. It is henceforth, our opinion, that motor coordination mechanisms should be fully integrated to robot controllers, allowing coordination, and thus social synchrony, when required. The aim of the work presented in this paper is to learn motor coordination with a human partner performing rhythmic movements. For that purpose, plastic Central Pattern Generators (CPG) are implemented in the joints of the Pepper robot. Hence, in this paper, we present an adaptive versatile model which can be used for any rhythmic movement and combination of joints. This is demonstrated with various arm movements.

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“…In other recent examples, using a very simple neural network providing minimal sensorimotor adaptation capabilities to the robot, unintentional motor coordination could emerge during an imitation game (of a simple gesture) with a human (Hasnain et al 2012; 2013). An extension of this work demonstrated that a robot could quickly and “online” learn more complex gestures and synchronize its behavior to the human partner based on the same sensorimotor approach (Ansermin et al 2016).…”

mentioning

confidence: 93%