A PhD position is available at the Inria Bordeaux Sud-Ouest center and the Institute of Neurodegenerative Disease in Bordeaux, France.

 

What: PhD position in computational neuroscience and robotics

Where: Inria, Bordeaux, France

When: October 2020 (3 years duration)

Who:   Xavier Hinaut & Frédéric Alexandre

Application deadline: May 22th (22/05/2020) How to apply: https://jobs.inria.fr/public/classic/en/offres/2020-02637  

 

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NewSpeak: Neuro-computational models of language comprehension and production grounded in robots.

 

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Recurrent Neural Network (RNN), Reservoir Computing, Developmental Language Learning, Neuro-Robotics, Multimodal Language Grounding, Computational Neuroscience, Reinforcement Learning

Candidate profile.

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-              Good background in maths and computer science;

-              A strong interest for neuroscience and the physiological processes underlying

learning;

-              Python programming with experience in scientific libraries Numpy/Scipy (or

similar coding language: matlab, etc.);

-              Experience in machine learning or data mining is a preferred;

-              Independence and ability to manage a project;

-              Good English reading/speaking skills.

 

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We target to embody models into robots that will developmentally ground language.

The grounding of semantics should come from the robot experiencing the world through its interactions with humans and the physical world. The goals are (1) to test hypotheses with biologically plausible language learning models with the Nao robot, (2) to extend the current model with unsupervised training and by reinforcement learning, and (3) to propose a new kind of Generative Adversarial Networks (GANs) for developmental language learning conditioned by grounded modalities such as vision.

In order to model how a sentence can be processed, word by word (Hinaut & Dominey 2013) or even phoneme by phoneme (Hinaut 2018), the use of recurrent (artificial) neural networks, such as Reservoir Computing, offers interesting advantages. In particular the possibility to compare the dynamics of the model with data from neuroscience experiments (EEG, fMRI, ...). This paradigm allows to learn with few learning examples, and offers negligible runtime for human-robot interactions.

The use of linguistic models with robots is not only useful to validate the models in real conditions, it also allows to test other hypotheses, notably on the anchoring of the language or the emergence of symbols. This involves finding out how a learning agent can link and categorise physical stimuli (vision, hearing, proprioception, etc.) to make the correspondence with symbols (Harnard 1990), or even to make these symbols emerge from stimuli coming from sensors (Taniguchi et al. 2016).

We aim that a robot could process language from morphemes to sentences, similarly as a child, in order to better model how children acquire language.

One aim is to obtain symbolic representations that are a composition of multimodal grounded representations. We will experiment how the newly developed language model will be able to learn to understand utterances by exploring which meanings the morphemes, words, ... can have based on other modalities of the robot (e.g. vision, proprioception). Starting from preliminary results (Juven & Hinaut 2020), we will first consider merging the representations from vision with a pre-trained CNN (Convolutional Neural Network). Then, reinforcement learning experiments will explore how the robot can learn the meaning of sentences: first by doing random actions for any user utterance, and then bootstrap from the user's feedback. We will use a concrete corpus of sentences based on actions a robot can do (Hinaut & Twiefel 2019). We will implement several variants of language models: (1) extension of the reservoir computing model linked with grounded CNN, (2) adapt such model to the GAN paradigm in order to couple language comprehension and production in a self-learning generative mechanism (thus creating more biologically plausible GANs), (3) explore unsupervised (cross-situational learning) and reinforcement learning with these models. In parallel, we will adapt models features and behaviours to the ones observed in language acquisition experiments in psychology, and neural evidences in neuro-linguistic studies. In particular, we will explore how the models could shed light on language developmental impairments. We will run models in simulated humanoid robots and in a Nao robot.

 

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More information is available on the application web page:

https://jobs.inria.fr/public/classic/en/offres/2020-02637 

Questions can be asked by email to Xavier Hinaut (xavier.hinaut@inria.fr).