World's Best Scientists 2026 revealed!
Olivier Sigaud

Olivier Sigaud

D-Index & Metrics

Computer Science

D-Index
34
Citations
4511
World Ranking
12235
National Ranking
309

Olivier Sigaud publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Olivier Sigaud sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 207 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Olivier Sigaud D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Olivier Sigaud sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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