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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
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In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
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Editorial: Language and Robotics
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In: ISSN: 2296-9144 ; Frontiers in Robotics and AI ; https://hal.inria.fr/hal-03533733 ; Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8, ⟨10.3389/frobt.2021.674832⟩ (2021)
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Cross-Situational Learning with Reservoir Computing for Language Acquisition Modelling
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In: 2020 International Joint Conference on Neural Networks (IJCNN 2020) ; https://hal.inria.fr/hal-02594725 ; 2020 International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, Scotland, United Kingdom ; https://wcci2020.org/ (2020)
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Language Acquisition with Echo State Networks: Towards Unsupervised Learning
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In: ICDL 2020 - IEEE International Conference on Development and Learning ; https://hal.inria.fr/hal-02926613 ; ICDL 2020 - IEEE International Conference on Development and Learning, Oct 2020, Valparaiso / Virtual, Chile (2020)
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A Journey in ESN and LSTM Visualisations on a Language Task
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In: https://hal.inria.fr/hal-03030248 ; 2020 (2020)
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Learning to Parse Grounded Language using Reservoir Computing
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In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
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| 9 |
Teach Your Robot Your Language! Trainable Neural Parser for Modelling Human Sentence Processing: Examples for 15 Languages
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In: ISSN: 2379-8920 ; EISSN: 2379-8939 ; IEEE Transactions on Cognitive and Developmental Systems ; https://hal.inria.fr/hal-01964541 ; IEEE Transactions on Cognitive and Developmental Systems, Institute of Electrical and Electronics Engineers, Inc, 2019, ⟨10.1109/TCDS.2019.2957006⟩ ; https://doi.org/10.1109/tcds.2019.2957006 (2019)
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A Reservoir Model for Intra-Sentential Code-Switching Comprehension in French and English
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In: CogSci'19 - 41st Annual Meeting of the Cognitive Science Society ; https://hal.inria.fr/hal-02432831 ; CogSci'19 - 41st Annual Meeting of the Cognitive Science Society, Jul 2019, Montréal, Canada ; https://cognitivesciencesociety.org/cogsci-2019/ (2019)
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| 11 |
Which Input Abstraction is Better for a Robot Syntax Acquisition Model? Phonemes, Words or Grammatical Constructions?
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In: 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) ; https://hal.inria.fr/hal-01889919 ; 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2018, Tokyo, Japan (2018)
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From Phonemes to Sentence Comprehension: A Neurocomputational Model of Sentence Processing for Robots
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In: SBDM2018 Satellite-Workshop on interfaces between Robotics, Artificial Intelligence and Neuroscience ; https://hal.inria.fr/hal-01964524 ; SBDM2018 Satellite-Workshop on interfaces between Robotics, Artificial Intelligence and Neuroscience, May 2018, Paris, France (2018)
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| 13 |
From Phonemes to Robot Commands with a Neural Parser
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In: IEEE ICDL-EPIROB Workshop on Language Learning ; https://hal.inria.fr/hal-01665823 ; IEEE ICDL-EPIROB Workshop on Language Learning, Sep 2017, Lisbon, Portugal. pp.1-2 (2017)
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Modelling sentence processing with random recurrent neural networks and applications to robotics
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In: Workshop "The role of the basal ganglia in the interaction between language and other cognitive functions" ; https://hal.inria.fr/hal-01673440 ; Workshop "The role of the basal ganglia in the interaction between language and other cognitive functions", Anne-Catherine Bachoud-Lévi, Maria Giavazzi, Charlotte Jacquemot, Laboratoire de NeuroPsychologie Interventionnelle., Oct 2017, Paris, France ; http://www.ens.fr/agenda/role-basal-ganglia-interaction-between-language-and-other-cognitive-functions/2017-10 (2017)
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| 15 |
Syntactic Reanalysis in Language Models for Speech Recognition
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In: 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) ; https://hal.inria.fr/hal-01558462 ; 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2017, Lisbon, Portugal ; http://icdl-epirob.org/ (2017)
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| 16 |
Teach Your Robot Your Language! Trainable Neural Parser for Modelling Human Sentence Processing: Examples for 15 Languages
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In: https://hal.inria.fr/hal-01665807 ; 2017 (2017)
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Recurrent Neural Network for Syntax Learning with Flexible Predicates for Robotic Architectures
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In: The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB) ; https://hal.inria.fr/hal-01417697 ; The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB), Sep 2016, Cergy, France ; http://icdl-epirob.org/ (2016)
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| 18 |
Recurrent Neural Network for Syntax Learning with Flexible Representations
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In: IEEE ICDL-EPIROB Workshop on Language Learning ; https://hal.inria.fr/hal-01417060 ; IEEE ICDL-EPIROB Workshop on Language Learning, Dec 2016, Cergy, France ; https://sites.google.com/site/epirob2016language/ (2016)
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Abstract:
International audience ; We present a Recurrent Neural Network (RNN), namely an Echo State Network (ESN), that performs sentence comprehension and can be used for Human-Robot Interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g. predicates). We have previously shown that this ESN is able to generalize to unknown sentence structures in English and French. The meaning representations it can learn to produce are flexible: it enables one to use any kind of " series of slots " (or more generally a vector representation) and are not limited to predicates. Moreover, preliminary work has shown that the model could be trained fully incrementally. Thus, it enables the exploration of language acquisition in a developmental approach. Furthermore, an " inverse " version of the model has been also studied, which enables to produce sentence structure from meaning representations. Therefore, if these two models are combined in a same agent, one can investigate language (and in particular syntax) emergence through agent-based simulations. This model has been encapsulated in a ROS module which enables one to use it in a cognitive robotic architecture, or in a distributed agent simulation.
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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.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences; Echo State Network; Human-Robot Interaction; Language acquisition; Recurrent Neural Network; Reservoir Computing; Syntax learning; Thematic Role Assignment
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URL: https://hal.inria.fr/hal-01417060/document
https://hal.inria.fr/hal-01417060/file/ICDL-Epirob_Workshop-on-language-learning_2016__paper-folder.pdf
https://hal.inria.fr/hal-01417060
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| 19 |
Reservoir Computing for Robot Language Acquisition
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In: IROS Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics ; https://hal.inria.fr/hal-01417683 ; IROS Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics, Oct 2016, Daejon, South Korea ; http://mlhlcr2016.tanichu.com/home (2016)
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| 20 |
Recurrent Neural Network Sentence Parser for Multiple Languages with Flexible Meaning Representations for Home Scenarios
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In: IROS Workshop on Bio-inspired Social Robot Learning in Home Scenarios ; https://hal.inria.fr/hal-01417667 ; IROS Workshop on Bio-inspired Social Robot Learning in Home Scenarios, Oct 2016, Daejon, South Korea ; https://www.informatik.uni-hamburg.de/wtm/SocialRobotsWorkshop2016/index.php (2016)
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