World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
4024
World Ranking
12769
National Ranking
625

Freek Stulp 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 Freek Stulp 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: 152 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Freek Stulp 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 Freek Stulp 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Freek Stulp is affiliated with the German Aerospace Center in Germany and has contributed extensively to the fields of engineering and computer science. Their research spans multiple subfields including control and systems engineering, biomedical engineering, computer vision and pattern recognition, mechanical engineering, and artificial intelligence.

The scientist's work focuses on a range of main topics related to robotics and automation. These include:

  • Robot Manipulation and Learning
  • Teleoperation and Haptic Systems
  • Reinforcement Learning in Robotics
  • Soft Robotics and Applications
  • Tactile and Sensory Interactions
  • Modular Robots and Swarm Intelligence
  • Robotic Locomotion and Control

Freek Stulp has published frequently in notable venues such as:

  • IEEE Robotics and Automation Letters
  • arXiv (Cornell University)
  • Frontiers in Robotics and AI
  • Experimental Brain Research
  • IEEE Robotics & Automation Magazine

Recent papers include:

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models, 2023, arXiv (Cornell University)
  • Object-Level Impedance Control for Dexterous In-Hand Manipulation, 2020, IEEE Robotics and Automation Letters
  • Pattern Recognition for Knowledge Transfer in Robotic Assembly Sequence Planning, 2020, IEEE Robotics and Automation Letters
  • A Digital Twin Approach for Contextual Assistance for Surgeons During Surgical Robotics Training, 2021, Frontiers in Robotics and AI
  • Sensorimotor performance and haptic support in simulated weightlessness, 2020, Experimental Brain Research

Freek Stulp has collaborated frequently with several coauthors, including:

  • Alin Albu-Schäffer
  • João Silvério
  • Gabriel Quere
  • Daniel Leidner
  • Jörn Vogel

The scientist's publication record reflects a diversity of topics and a strong presence in robotics research, particularly in manipulation, haptics, and learning systems. Their contributions intersect both theoretical and applied research relevant to advancing autonomous and assistive robotic technologies.

Best Publications

  • Learning variable impedance control

    Jonas Buchli;Freek Stulp;Evangelos Theodorou;Stefan Schaal

  • Reinforcement Learning With Sequences of Motion Primitives for Robust Manipulation

    F. Stulp;E. A. Theodorou;S. Schaal

  • Many regression algorithms, one unified model

    Freek Stulp;Olivier Sigaud

  • Path Integral Policy Improvement with Covariance Matrix Adaptation

    Freek Stulp;Freek Stulp;Olivier Sigaud

  • Robot Skill Learning: From Reinforcement Learning to Evolution Strategies

    Freek Stulp;Olivier Sigaud

  • A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials

    Konstantinos Chatzilygeroudis;Vassilis Vassiliades;Freek Stulp;Sylvain Calinon

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Unknown

  • Learning to grasp under uncertainty

    Freek Stulp;Evangelos Theodorou;Jonas Buchli;Stefan Schaal

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

    Unknown

  • Hierarchical reinforcement learning with movement primitives

    Freek Stulp;Stefan Schaal

  • Movement segmentation using a primitive library

    Franziska Meier;Evangelos Theodorou;Freek Stulp;Stefan Schaal

  • The Assistive Kitchen — A demonstration scenario for cognitive technical systems

    M. Beetz;F. Stulp;B. Radig;J. Bandouch

  • From dynamic movement primitives to associative skill memories

    Peter Pastor;Mrinal Kalakrishnan;Franziska Meier;Freek Stulp

  • Variable Impedance Control - A Reinforcement Learning Approach

    Jonas Buchli;Evangelos A. Theodorou;Freek Stulp;Stefan Schaal

  • 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

  • Generality and legibility in mobile manipulation

    Michael Beetz;Freek Stulp;Piotr Esden-Tempski;Andreas Fedrizzi

  • Learning Local Objective Functions for Robust Face Model Fitting

    M. Wimmer;F. Stulp;S. Pietzsch;B. Radig

  • Model-Free Reinforcement Learning of Impedance Control in Stochastic Environments

    F. Stulp;J. Buchli;A. Ellmer;M. Mistry

  • Reinforcement learning of full-body humanoid motor skills

    Freek Stulp;Jonas Buchli;Evangelos Theodorou;Stefan Schaal

  • Iteratively Refined Feasibility Checks in Robotic Assembly Sequence Planning

    Ismael Rodriguez;Korbinian Nottensteiner;Daniel Leidner;Michael Kasecker

  • Learning and reasoning with action-related places for robust mobile manipulation

    Freek Stulp;Andreas Fedrizzi;Lorenz Mösenlechner;Michael Beetz

Frequent Co-Authors

Michael Beetz
Michael Beetz University of Bremen
Olivier Sigaud
Olivier Sigaud Sorbonne University
Stefan Schaal
Stefan Schaal Google (United States)
Evangelos A. Theodorou
Evangelos A. Theodorou Georgia Institute of Technology
Pierre-Yves Oudeyer
Pierre-Yves Oudeyer French Institute for Research in Computer Science and Automation - INRIA
Jonas Buchli
Jonas Buchli DeepMind (United Kingdom)
Alin Albu-Schaffer
Alin Albu-Schaffer German Aerospace Center
Jean-Baptiste Mouret
Jean-Baptiste Mouret University of Lorraine
Timothy M. Hospedales
Timothy M. Hospedales University of Edinburgh
Sylvain Calinon
Sylvain Calinon Idiap Research Institute

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