Skip to Main content Skip to Navigation
Journal articles

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

Abstract : Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning.
Document type :
Journal articles
Complete list of metadata

Cited literature [50 references]  Display  Hide  Download

https://hal.sorbonne-universite.fr/hal-01279447
Contributor : Gestionnaire Hal-Upmc <>
Submitted on : Friday, February 26, 2016 - 11:11:09 AM
Last modification on : Tuesday, June 1, 2021 - 11:46:12 AM

File

srep19908.pdf
Publication funded by an institution

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Sofiane Boucenna, David Cohen, Andrew N. Meltzoff, Philippe Gaussier, Mohamed Chetouani. Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars. Scientific Reports, Nature Publishing Group, 2016, 6, pp.19908. ⟨10.1038/srep19908⟩. ⟨hal-01279447⟩

Share

Metrics

Record views

455

Files downloads

377