World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
93
Citations
33519
World Ranking
517
National Ranking
277

Ken Goldberg 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 Ken Goldberg 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: 575 publications — 95th percentile

95% of scientists in this discipline score the same or lower.

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

Ken Goldberg 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 Ken Goldberg 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: 93 D-Index — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Overview

Ken Goldberg is affiliated with the University of California, Berkeley in the United States. Their research spans multiple fields within computer science and engineering, with a significant focus on robotics.

Their main fields of study include:

  • Computer Science
  • Engineering

Within these broader areas, Ken Goldberg has contributed to several subfields, including:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Biomedical Engineering
  • Control and Systems Engineering
  • Surgery

The primary topics of their work reflect a focus on robotics and technology related to human-machine interaction and surgical applications. Key topics include:

  • Robot Manipulation and Learning
  • Robotic Path Planning Algorithms
  • Reinforcement Learning in Robotics
  • Augmented Reality Applications
  • Surgical Simulation and Training
  • Anatomy and Medical Technology
  • Soft Robotics and Applications

They have published extensively across various respected venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Science Robotics
  • IEEE Transactions on Automation Science and Engineering
  • Nature Machine Intelligence
  • 2022 International Conference on Robotics and Automation (ICRA)

Their recent papers include the following titles with publication year and venues:

  • Mechanical Search on Shelves using a Novel "Bluction" Tool, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • Augmented dexterity: How robots can enhance human surgical skills, 2024, Science Robotics
  • Automated Pruning and Irrigation of Polyculture Plants, 2024, IEEE Transactions on Automation Science and Engineering
  • AI reflections in 2019, 2020, Nature Machine Intelligence
  • A roadmap for AI in robotics, 2025, Nature Machine Intelligence

Ken Goldberg frequently collaborates with several co-authors, notably:

  • Chung Min Kim
  • Letian Fu
  • Kush Hari
  • Frank C. Park
  • Pieter Abbeel

In addition to journal and conference papers, they have a contribution in book publishing, including a title published by Springer International Publishing:

  • Algorithmic Foundations of Robotics XII, 2020

Best Publications

  • Eigentaste: A Constant Time Collaborative Filtering Algorithm

    Ken Goldberg;Theresa Roeder;Dhruv Gupta;Chris Perkins

  • Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

    Jeffrey Mahler;Jacky Liang;Sherdil Niyaz;Michael Laskey

  • A Survey of Research on Cloud Robotics and Automation

    Ben Kehoe;Sachin Patil;Pieter Abbeel;Ken Goldberg

  • Orienting polygonal parts without sensors

    Kenneth Y. Goldberg

  • Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

    Tianhao Zhang;Zoe McCarthy;Owen Jowl;Dennis Lee

  • Learning ambidextrous robot grasping policies

    Jeffrey Mahler;Matthew Matl;Vishal Satish;Michael Danielczuk

  • Combating COVID-19-The role of robotics in managing public health and infectious diseases.

    Guang-Zhong Yang;Bradley J. Nelson;Robin R. Murphy;Howie Choset

  • LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information

    Jur Van Den Berg;Pieter Abbeel;Ken Goldberg

  • Desktop teleoperation via the World Wide Web

    K. Goldberg;M. Mascha;S. Gentner;N. Rothenberg

  • A television event marking system

    Gregory H. Kelly;Kenneth Y. Goldberg;John S. Gee;Philip D. Levinson

  • Respectful cameras: detecting visual markers in real-time to address privacy concerns

    J. Schiff;M. Meingast;D.K. Mulligan;S. Sastry

  • Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards

    Jeffrey Mahler;Florian T. Pokorny;Brian Hou;Melrose Roderick

  • Cloud-based robot grasping with the google object recognition engine

    Ben Kehoe;Akihiro Matsukawa;Sal Candido;James Kuffner

  • Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

    Jeffrey Mahler;Matthew Matl;Xinyu Liu;Albert Li

  • RLlib: Abstractions for Distributed Reinforcement Learning

    Eric Liang;Richard Liaw;Philipp Moritz;Robert Nishihara

  • Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

    Jeffrey Mahler;Jacky Liang;Sherdil Niyaz;Michael Laskey

  • The stochastic motion roadmap: A sampling framework for planning with Markov motion uncertainty

    Ron Alterovitz;Thierry Siméon;Kenneth Y. Goldberg

  • RLlib: Abstractions for Distributed Reinforcement Learning

    Eric Liang;Richard Liaw;Robert Nishihara;Philipp Moritz

  • The Robot in the Garden: Telerobotics and Telepistemology in the Age of the Internet

    Ken Goldberg;Hubert Dreyfus;Alvin Goldman;Oliver Grau

  • Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations

    Jur van den Berg;Stephen Miller;Daniel Duckworth;Humphrey Hu

  • ViBE: a compressed video database structured for active browsing and search

    C. Taskiran;Jau-Yuen Chen;A. Albiol;L. Torres

  • Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

    Tianhao Zhang;Zoe McCarthy;Owen Jow;Dennis Lee

Frequent Co-Authors

Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Sachin Patil
Sachin Patil University of California, Berkeley
Ron Alterovitz
Ron Alterovitz University of North Carolina at Chapel Hill
Animesh Garg
Animesh Garg University of Toronto
Joseph E. Gonzalez
Joseph E. Gonzalez University of California, Berkeley
John Canny
John Canny University of California, Berkeley
Ion Stoica
Ion Stoica University of California, Berkeley
Jur van den Berg
Jur van den Berg University of Utah
Michael J. Franklin
Michael J. Franklin University of Chicago
Allison M. Okamura
Allison M. Okamura Stanford University

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