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21
Semantic Role Labelling for Robot Instructions using Echo State Networks
In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) ; https://hal.inria.fr/hal-01417701 ; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2016, Bruges, Belgium ; https://www.elen.ucl.ac.be/esann/index.php?pg=esann16_programme (2016)
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22
Using Natural Language Feedback in a Neuro-inspired Integrated Multimodal Robotic Architecture
In: 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) ; https://hal.inria.fr/hal-01417706 ; 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Aug 2016, New York City, United States. pp.52 - 57, ⟨10.1109/ROMAN.2016.7745090⟩ ; http://www.tc.columbia.edu/conferences/roman2016/ (2016)
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23
A Recurrent Neural Network for Multiple Language Acquisition: Starting with English and French
In: Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (CoCo 2015) ; https://hal.inria.fr/hal-02561258 ; Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (CoCo 2015), Dec 2015, Montreal, Canada ; http://ceur-ws.org/Vol-1583/ (2015)
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24
Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks
In: ISSN: 1662-5218 ; EISSN: 1662-5218 ; Frontiers in Neurorobotics ; https://hal.inria.fr/hal-02383530 ; Frontiers in Neurorobotics, Frontiers, 2014, 8, ⟨10.3389/fnbot.2014.00016⟩ (2014)
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25
On-Line Learning of Lexical Items and Grammatical Constructions via Speech, Gaze and Action-Based Human-Robot Interaction
In: INTERSPEECH 2013 - 14th Annual Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-02561340 ; INTERSPEECH 2013 - 14th Annual Conference of the International Speech Communication Association, Aug 2013, Lyon, France ; https://www.isca-speech.org/archive/interspeech_2013/i13_2657.html (2013)
Abstract: International audience ; In order to be able to understand a conversation in interaction, a robot, has to first understand the language used by his interlocutor. A central aspect of language learning is adaptability. Individuals can learn new words and new grammatical structures. We have developed learning methods that allow the hu-manoid robot iCub to robot can learn new lexical items by interaction with the human and consolidation of its autobiographical memory. Then, based on these open class words, the robot can bootstrap the acquisition of novel grammatical structures in real-time. Finally, we demonstrate how human gaze can be monitored, and could be used in order to reduce referential ambiguity inherent in such learning conditions. These learning capabilities are demonstrated in a collection of videos.
Keyword: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; [SCCO.LING]Cognitive science/Linguistics; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
URL: https://hal.inria.fr/hal-02561340
https://hal.inria.fr/hal-02561340/file/Pointeau2013.pdf
https://hal.inria.fr/hal-02561340/document
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26
Online Language Learning to Perform and Describe Actions for Human-Robot Interaction
In: Post-Graduate Conference on Robotics and Development of Cognition ; https://hal.inria.fr/hal-02561346 ; Post-Graduate Conference on Robotics and Development of Cognition, Sep 2012, Lausanne, Switzerland (2012)
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