About me

Since october 2011, I am a PhD Student in the Equipes Traitement de l'Information et Systèmes (ETIS) laboratory [Information and System Processing Teams] in Cergy-Pontoise, and a member of the neurocybernetics team. My supervisor is Pr.Mathias Quoy, a professor in the university of Cergy-Pontoise, in addition to Pr.Philippe Gaussier, the director of the team.
The title of my PhD thesis is "Toward a plausible model of action selection for a mobile robot". The main topic of the thesis is to develop actions selection models bio-inspired based on hippocampus, prefrontal cortex and basal ganglia modelisations. My thesis si a part of the ANR project Neurobot.

About Neurocybernetics team

The research activity of the team aims at understanding the mecanisms enabling a living being to adapt to its environment (insect, animal or human) and at implementing them on autonomous robots using vision as main source of information. This bottom-up approch explores the idea that, in a rich enough environment, stable complex behaviors could emerge from the low level mecanisms of the robot and its abilities to adapt to the environment. To do so, the dynamics of the robotic system as well as the global dynamic of the interactions, between the system(s) and the environment - whether physical or social, are considered. Sensorimotor loops are designed as artificial neural networks with capabilities for learning (associative, unsupervised, conditionning or reinforcement learning), and for parallelization (splitting and distributing the neural loops over several computing units, reusing the same cortical and sub-cortical "structures" in several models). Thus, the same tools are used to study the issues of motor control, multi-modality, planning and selection of action for navigation and non-verbal human-robot cooperation tasks requiring the understanding of the mecanisms of recognition, affordance, imitation and emotional interactions.

Research interests

Bio-inpired models for mobile robots navigation, robotic arms manipulation and related questions:

  • Neural networks, bio-inspired robotics
  • Learning from demonstration, reinforcement learning, autonomous learning
  • Developmental approach
  • Sensorimotor control, navigation
  • Hippocampus, frontal cortex and basal ganglia modelisation