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Stephane Doncieux

  • Dr.
  • Associate Professor
  • ISIR/UPMC
  • 19h-indexImpact measure calculated using publication and citation counts. Updated daily.
  • 1374CitationsNumber of citations received by Stephane's publications. Updated daily.

Recent publications

  • Bootstrapping Q-Learning for Robotics from Neuro-Evolution Results

    • Zimmer M
    • Doncieux S
    N/AReaders
    3Citations
    Get full text
  • Experimental testing and simulations of speed variations impact on fuel consumption of conventional gasoline passenger cars

    • Freitas Salgueiredo C
    • Orfila O
    • Saint Pierre G
    • et al.
    N/AReaders
    2Citations
    Get full text

Professional experience

Associate Professor

ISIR/UPMC

October 2004 - Present

Education

PhD

UPMC

October 1999 - June 2003(4 years)

DEA IARFA (master)

UPMC

September 1998 - September 1999(a year)

Engineer

ENSEA

September 1996 - August 1999(3 years)

About

Stephane Doncieux is associate professor at the UPMC since 2004. He defended its HDR (French post-doctoral degree allowing its holder to supervise PhD students) on evolutionary robotics in 2010. Its PhD, defended in 2003, was supervised by Jean-Arcady Meyer at the AnimatLab and dealt with the use of evolutionary robotics methods in the context of flying robots. He is now the coordinator of the SIMA research team with its colleague Bruno Gas. His field of research is evolutionary robotics, i.e. the use of optimization algorithms inspired from natural selection for the design of robot controllers on the basis of a description of desired behavior effects. Such tools are now also used to help other scientists to optimize and analyse their models (computational neuroscientists, fluid mechanics experts). Its current researches are focused on the use of multi-objective approaches to either enhance the evolutionary search or provide a tool for computational model analysis. He particularly focuses his work on multiobjectivizationin the context of evolutionary robotics, with the definition of objectives like, for instance, behavioral diversity (to limit premature convergence), transferability (to avoid the reality gap between simulation and reality) or an objective on module functions (exaptation approach). He also draws inspiration from computational neuroscience to synthetize more cognitive neurocontrollers.

Groups

  • IREC
    IREC

Co-authors (54)

Other IDs