I worked at the Cergy-Pontoise University as a research and teaching assistant during two years.
I did my Ph.D. "in co-tutelle" between Université de Cergy-Pontoise and Donetsk National Technical University (France/Ukraine) in 2014 with Pr. Patrick Hénaff (Neurocybernetics Team of Prof. Philippe GAUSSIER) and Pr. Volodymyr Borysenko.
My previous research was focused on (1) human-robot physical interaction and its quantitative analysis (2), (3) tactile perception for the robotic arm.
My actual research is focused on rehabilitation robotics and its coupling with virtual or immersive reality (4) , (5) motion tracking for gait rehabilitation, the biologically inspired robot control (6) and tensegrity robots (7).
Keywords: bio-inspired control, physical human-robot interaction, humanoid robot control, measurement, control, scientific instrumentation.
--- Technical and Personal skills ---
Development and simulation: C/C++, Matlab, Python. Basic ability: R, Qt5, git, CMake, Java.
Development of the applications for signal processing, for data acquisition or for automation/control purpose.
Industry Software: Proficient in: Matlab/Simulink. Basic ability: LabView, DesignSpark, Quartus II.
Mathematical modeling of complex industrial systems for analysis or control.
VR and Immersive technologies: Basic ability: Unity, VTK, Blender.
3-D virtual world creation for Immersive Environment with gamification.
Internet of Things: Basic ability with: Spark (Photon), Raspberry PI, Arduino Yun.
Development of basic interactive system to communicate between sensor and master program using device API.
Micro-controllers: Proficient in: Atmel AVR, ARM and Phidgets. Basic ability with: Feather, Cyclone II.
Realization of several microprocessor real-world applications for human motion measure or robot control.
Sensors: Proficient in: Inertial sensors, Force/Tactile Sensors, Distance Sensors, Basic ability: Kinect.
Sensor integration to the control loop and building real-world robotic applications for control or interaction
Automation and Robotics: Position control, Admittance or Impedance Control, Neural Control.
Analyzing and Reporting data: Exploratory data analysis, Reports, State-of-the-Art, Scientific Writing.
Teamwork: Contributing, Cooperation, Expanding Ideas, Research
--- Previous Employment ---
--- List of my Professional Development Courses & Training Seminars ---
C++ Software Development: White belt and Yellow belt. by Moscow Institute of Physics and Technology & Yandex on Coursera (2019).
Unity Basics (2018)
Deep Learning Spring School Deep Learning Spring School (2017)
Object oriented programming C++ (2016);
In this poster, we outline a NN model for trajectories learning and prediction online, i.e to learn without knowing the future of the trajectory. Our model is based on the idea that a trajectory can be learned and recognized by chunks.
Each chunk is learned by a prediction unit (PU). Hence, our problem can be divided in two parts : (1) how a PU can learn and recognize online a given chunk as a temporal category; and (2) how a global architecture allow the competition of many prediction units to provide the prediction of the whole trajectory thru time.