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Olivier Sigaud

Olivier Sigaud

D-Index & Metrics

Computer Science

D-Index
34
Citations
4511
World Ranking
12237
National Ranking
309

Overview

Olivier Sigaud is affiliated with Sorbonne University in France and has a research focus primarily within the field of Computer Science, contributing notably to Artificial Intelligence and its subfields. Their work spans across several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Biomedical Engineering, and Mechanical Engineering.

The scientist's main topics of research cover diverse areas such as Reinforcement Learning in Robotics, Robot Manipulation and Learning, Natural Language Processing Techniques, Evolutionary Algorithms and Applications, Modular Robots and Swarm Intelligence, Domain Adaptation and Few-Shot Learning, and Robotic Path Planning Algorithms.

Olivier Sigaud has published extensively, with a substantial number of contributions appearing in venues such as:

  • arXiv (Cornell University)
  • Communications Biology
  • HAL (Le Centre pour la Communication Scientifique Directe)
  • Journal of Artificial Intelligence Research
  • ACM Transactions on Evolutionary Learning and Optimization

Recent research papers authored or co-authored by Olivier Sigaud include:

  • "Combining Evolution and Deep Reinforcement Learning for Policy Search: A Survey," 2022, ACM Transactions on Evolutionary Learning and Optimization
  • "Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: A Short Survey," 2022, Journal of Artificial Intelligence Research
  • "Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning," 2023, arXiv (Cornell University)
  • "Interactively shaping robot behaviour with unlabeled human instructions," 2020, Autonomous Agents and Multi-Agent Systems
  • "Grounding Language to Autonomously-Acquired Skills via Goal Generation," 2020, arXiv (Cornell University)

The scientist collaborates regularly with several frequent co-authors, including:

  • Mohamed Chétouani
  • Cédric Colas
  • Pierre-Yves Oudeyer
  • Nicolas Perrin-Gilbert
  • Ahmed Akakzia

Best Publications

  • Many regression algorithms, one unified model

    Freek Stulp;Olivier Sigaud

  • Path Integral Policy Improvement with Covariance Matrix Adaptation

    Freek Stulp;Freek Stulp;Olivier Sigaud

  • On-line regression algorithms for learning mechanical models of robots: A survey

    Olivier Sigaud;Camille Salaün;Vincent Padois

  • Robot Skill Learning: From Reinforcement Learning to Evolution Strategies

    Freek Stulp;Olivier Sigaud

  • Learning the structure of Factored Markov Decision Processes in reinforcement learning problems

    Thomas Degris;Olivier Sigaud;Pierre-Henri Wuillemin

  • Anticipatory Behavior in Adaptive Learning Systems

    Giovanni Pezzulo;Martin V. Butz;Olivier Sigaud;Gianluca Baldassarre

  • Learning classifier systems: a survey

    Olivier Sigaud;Stewart W. Wilson

  • Reinforcement Learning

    Unknown

  • Anticipatory Behavior in Adaptive Learning Systems: Foundations, Theories, and Systems

    Martin V. Butz;Olivier Sigaud;Pierre Gérard

  • Anticipatory Behavior: Exploiting Knowledge About the Future to Improve Current Behavior

    Martin V. Butz;Martin V. Butz;Olivier Sigaud;Pierre Gérard

  • Internal Models and Anticipations in Adaptive Learning Systems

    Martin V. Butz;Martin V. Butz;Olivier Sigaud;Pierre Gérard

  • CEM-RL: Combining evolutionary and gradient-based methods for policy search.

    Aloïs Pourchot;Olivier Sigaud

  • CEM-RL: Combining evolutionary and gradient-based methods for policy search

    Aloïs Pourchot;Olivier Sigaud

  • GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

    Cédric Colas;Olivier Sigaud;Pierre-Yves Oudeyer

  • Markov Decision Processes in Artificial Intelligence

    Olivier Sigaud;Olivier Buffet

  • Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: A Short Survey

    Unknown

  • Modelling Individual Differences in the Form of Pavlovian Conditioned Approach Responses: A Dual Learning Systems Approach with Factored Representations

    Florian Lesaint;Olivier Sigaud;Shelly B. Flagel;Shelly B. Flagel;Terry E. Robinson

  • From Motor Learning to Interaction Learning in Robots

    Olivier Sigaud;Jan Peters

  • Object Learning Through Active Exploration

    Serena Ivaldi;Sao Mai Nguyen;Natalia Lyubova;Alain Droniou

  • Learning compact parameterized skills with a single regression

    Freek Stulp;Gennaro Raiola;Antoine Hoarau;Serena Ivaldi

  • Policy Improvement Methods: Between Black-Box Optimization and Episodic Reinforcement Learning

    Freek Stulp;Olivier Sigaud

  • Policy search in continuous action domains: An overview.

    Olivier Sigaud;Freek Stulp

  • How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments

    Cédric Colas;Olivier Sigaud;Pierre-Yves Oudeyer

  • GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

    Cédric Colas;Olivier Sigaud;Pierre-Yves Oudeyer

  • CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

    Cédric Colas;Pierre Fournier;Olivier Sigaud;Mohamed Chetouani

Frequent Co-Authors

Mohamed Chetouani
Mohamed Chetouani Sorbonne University
Pierre-Yves Oudeyer
Pierre-Yves Oudeyer French Institute for Research in Computer Science and Automation - INRIA
Freek Stulp
Freek Stulp German Aerospace Center
Martin V. Butz
Martin V. Butz University of Tübingen
Giovanni Pezzulo
Giovanni Pezzulo National Academies of Sciences, Engineering, and Medicine
Gianluca Baldassarre
Gianluca Baldassarre National Academies of Sciences, Engineering, and Medicine
Timothy M. Hospedales
Timothy M. Hospedales University of Edinburgh
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Francesco Nori
Francesco Nori DeepMind (United Kingdom)
Shelly B. Flagel
Shelly B. Flagel University of Michigan–Ann Arbor

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