World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
10409
World Ranking
5857
National Ranking
271

Marc Toussaint 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 Marc Toussaint 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: 254 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Marc Toussaint 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 Marc Toussaint 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Marc Toussaint is affiliated with the Technical University of Berlin in Germany. Their research spans various fields and subfields of study within computer science and engineering, focusing notably on robotics and artificial intelligence.

The main fields of study in their work include:

  • Computer Science
  • Engineering

Subfields of study encompass:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Systems Engineering
  • Computer Networks and Communications
  • Aerospace Engineering

The core topics of research are:

  • Robotic Path Planning Algorithms
  • Robot Manipulation and Learning
  • AI-based Problem Solving and Planning
  • Reinforcement Learning in Robotics
  • Human Pose and Action Recognition
  • Machine Learning and Algorithms
  • Robotics and Sensor-Based Localization

Marc Toussaint has authored multiple papers published in a range of venues. Selected recent papers include:

  • "PaLM-E: An Embodied Multimodal Language Model" (2023), arXiv (Cornell University)
  • "Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly" (2022), IEEE Transactions on Robotics
  • "MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets" (2021), IEEE Robotics and Automation Letters
  • "Describing Physics For Physical Reasoning: Force-Based Sequential Manipulation Planning" (2020), IEEE Robotics and Automation Letters
  • "From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence" (2021), arXiv (Cornell University)

Frequent publication venues for Toussaint include:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • IEEE Transactions on Robotics
  • The International Journal of Robotics Research

Their work features collaboration with several coauthors such as:

  • Andreas Orthey
  • Danny Driess
  • Joaquim Ortiz-Haro
  • Valentin N. Hartmann
  • Ozgur S. Oguz

Best Publications

  • PaLM-E: An Embodied Multimodal Language Model

    Unknown

  • Using Machine Learning to Focus Iterative Optimization

    F. Agakov;E. Bonilla;J. Cavazos;B. Franke

  • Probabilistic inference for solving discrete and continuous state Markov Decision Processes

    Marc Toussaint;Amos Storkey

  • Robot trajectory optimization using approximate inference

    Marc Toussaint

  • Planning as inference

    Matthew Botvinick;Marc Toussaint

  • Extracting Motion Primitives from Natural Handwriting Data

    Ben H. Williams;Marc Toussaint;Amos J. Storkey

  • Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning.

    Marc Toussaint;Kelsey R. Allen;Kevin A. Smith;Joshua B. Tenenbaum

  • On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference

    Konrad Rawlik;Marc Toussaint;Sethu Vijayakumar

  • Logic-geometric programming: an optimization-based approach to combined task and motion planning

    Marc Toussaint

  • Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress

    Manuel Lopes;Tobias Lang;Marc Toussaint;Pierre-yves Oudeyer

  • Multi-class image segmentation using conditional random fields and global classification

    Nils Plath;Marc Toussaint;Shinichi Nakajima

  • Gaussian process implicit surfaces for shape estimation and grasping

    Stanimir Dragiev;Marc Toussaint;Michael Gienger

  • A No-Free-Lunch Theorem for Non-Uniform Distributions of Target Functions

    Christian Igel;Marc Toussaint

  • Probabilistic inference as a model of planned behavior.

    Marc Toussaint

  • On classes of functions for which No Free Lunch results hold

    Christian Igel;Marc Toussaint

  • Hierarchical POMDP controller optimization by likelihood maximization

    Marc Toussaint;Laurent Charlin;Pascal Poupart

  • Probabilistic inference for solving (PO) MDPs

    Marc Toussaint;Stefan Harmeling;Amos Storkey

  • Inverse KKT: Learning cost functions of manipulation tasks from demonstrations:

    Peter Englert;Ngo Anh Vien;Marc Toussaint

  • Safe Exploration for Active Learning with Gaussian Processes

    Jens Schreiter;Duy Nguyen-Tuong;Mona Eberts;Bastian Bischoff

  • Learning model-free robot control by a Monte Carlo EM algorithm

    Nikos Vlassis;Marc Toussaint;Georgios Kontes;Savas Piperidis

  • Probabilistic Recurrent State-Space Models

    Andreas Doerr;Christian Daniel;Martin Schiegg;Duy Nguyen-Tuong

  • International Joint Conference in Artificial Intelligence (IJCAI)

    Konrad Rawlik;Marc Toussaint;Sethu Vijayakumar

  • Proc. of the 19th. International Joint Conference on Artificial Intelligence (IJCAI'05)

    M. Toussaint;Sethu Vijayakumar

Frequent Co-Authors

Sethu Vijayakumar
Sethu Vijayakumar University of Edinburgh
Manuel Lopes
Manuel Lopes Instituto Superior Técnico
Christian Igel
Christian Igel University of Copenhagen
Stefan Schaal
Stefan Schaal Google (United States)
Michael Gienger
Michael Gienger Honda (Japan)
Amos Storkey
Amos Storkey University of Edinburgh
Pascal Poupart
Pascal Poupart University of Waterloo
Oliver Brock
Oliver Brock Technical University of Berlin
Wolfgang Maass
Wolfgang Maass Graz University of Technology
Shlomo Zilberstein
Shlomo Zilberstein University of Massachusetts Amherst

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