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Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics ...
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LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading ...
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Neural Network Learning for Robust Speech Recognition
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Qu, Leyuan. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2021
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Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives ...
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Conversational Language Learning for Human-Robot Interaction
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Abstract:
Language is one of the complex but fascinating ways of communication, and it is continuously developed and maintained in the human brain. It is remarkable to study how humans understand each other in a conversation and continually learn and develop their communication skills. Understanding the meaning of the spoken or written language and interacting in that language differentiates humans from other species. Although it is difficult to define the exact working nature of the brain related to language acquisition and development, researchers find a strong relationship between different behaviours acquired based on social, cognitive, emotional and behavioural intelligence. Social robots and artificial human-like intelligent agents are the expected members of future society, where they are firmly expected to realize and exhibit verbal communication capability. In addition to the robot appearance, conversational understanding and behaviours are crucial aspects for their acceptance and co-existence in emerging society. This thesis aims to connect the knowledge from behavioural intelligence through conversational language learning with human-robot interaction (HRI). The socio-linguistic features, such as emotion, sentiment, politeness and dialogue acts, are the building blocks of the decision-making process in humans. This thesis presents extensive conversational analysis through artificial recurrent neural modelling that helps to build the robots aware of such linguistic cues. Accordingly, the thesis provides tools to analyze and investigate language on different aspects using recurrent neural networks (RNNs) and attention mechanism and eventually demonstrates an HRI scenario that facilitates robotics behavioural adaptation based on social cues. As a result, the thesis provides insights into the conversational analysis with emotion and dialogue acts, providing useful knowledge of natural language understanding for safe human-robot interaction. The primary contribution to knowledge from the study and experiments provided in this thesis is understanding the socio-linguistic features, with the motive of developing a natural language conversational system for HRI. The analytical experiments in this thesis can inform necessary future work in order to integrate social cues for robotic behavioural adaptation. Furthermore, this thesis provides knowledge to realize safer social robots in society with verbal communication capability using computational neural linguistics approaches, along with addressing the safety concerns of humans.
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Keyword:
004: Informatik; Artificial Intelligence; Conversational Analysis; ddc:004:; Emotion; Natural Language Processing; Recurrent Neural Networks; Sentiment and Politeness; Social Robots
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URL: http://nbn-resolving.de/urn:nbn:de:gbv:18-ediss-90853
https://ediss.sub.uni-hamburg.de/handle/ediss/8884
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Crossmodal Language Grounding in an Embodied Neurocognitive Model
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In: Front Neurorobot (2020)
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Incorporating End-to-End Speech Recognition Models for Sentiment Analysis ...
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Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robots ...
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GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection ...
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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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Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture ...
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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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Semantic Role Labelling for Robot Instructions using Echo State Networks
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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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Using Natural Language Feedback in a Neuro-inspired Integrated Multimodal Robotic Architecture
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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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Natural language acquisition in recurrent neural architectures ; Erwerb von natürlicher Sprache in rekurrenten neuronalen Architekturen
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Heinrich, Stefan. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2016
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A Recurrent Neural Network for Multiple Language Acquisition: Starting with English and French
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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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Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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