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Animesh Garg

D-Index & Metrics

Computer Science

D-Index
40
Citations
6079
World Ranking
9368
National Ranking
372

Animesh Garg 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 Animesh Garg 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 201 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Animesh Garg 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 Animesh Garg sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Animesh Garg is affiliated with the University of Toronto in Canada. Their research spans multiple areas within computer science and engineering, with a particular focus on artificial intelligence and robotics.

Their main fields of study include:

  • Computer Science
  • Engineering

Within these fields, they specialize in several subfields, notably:

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

The major topics their work addresses encompass:

  • Reinforcement Learning in Robotics
  • Robot Manipulation and Learning
  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Modular Robots and Swarm Intelligence
  • Robotic Locomotion and Control

They have contributed extensively to academic publications, with frequent appearances in the following venues:

  • arXiv (Cornell University)
  • Autonomous Robots
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Recent significant papers authored or co-authored include:

  • "X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments," 2023, IEEE Robotics and Automation Letters
  • "Large language models for chemistry robotics," 2023, Autonomous Robots
  • "Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation," 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • "ORGANA: A robotic assistant for automated chemistry experimentation and characterization," 2024, Matter

The scientist has collaborated frequently with several individuals, including:

  • Florian Shkurti
  • Dieter Fox
  • Samarth Sinha
  • Alán Aspuru-Guzik
  • Homanga Bharadhwaj

Best Publications

  • ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

    Unknown

  • Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Krishnan Srinivasan;Parth Shah

  • Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning:

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

  • Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

    Adithyavairavan Murali;Siddarth Sen;Ben Kehoe;Animesh Garg

  • Learning task-oriented grasping for tool manipulation from simulated self-supervision:

    Kuan Fang;Yuke Zhu;Animesh Garg;Animesh Garg;Andrey Kurenkov

  • Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

    Siddarth Sen;Animesh Garg;David V. Gealy;Stephen McKinley

  • Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Peter Zachares;Matthew Tan

  • Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

    Danfei Xu;Suraj Nair;Yuke Zhu;Julian Gao

  • X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

    Unknown

  • Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning

    Brijen Thananjeyan;Animesh Garg;Sanjay Krishnan;Carolyn Chen

  • Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks

    Roberto Martin-Martin;Michelle A. Lee;Rachel Gardner;Silvio Savarese

  • Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration

    De-An Huang;Suraj Nair;Danfei Xu;Yuke Zhu

  • Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

    Ajay Mandlekar;Yuke Zhu;Animesh Garg;Li Fei-Fei

  • DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

    Andrey Kurenkov;Jingwei Ji;Animesh Garg;Viraj Mehta

  • ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

    Ajay Mandlekar;Yuke Zhu;Animesh Garg;Jonathan Booher

  • Weakly Supervised 3D Reconstruction with Adversarial Constraint

    JunYoung Gwak;Christopher B. Choy;Manmohan Chandraker;Animesh Garg

  • Transition State Clustering: Unsupervised Surgical Trajectory Segmentation for Robot Learning.

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

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

    Unknown

  • Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos

    De-An Huang;Shyamal Buch;Lucio Dery;Animesh Garg

  • A single-use haptic palpation probe for locating subcutaneous blood vessels in robot-assisted minimally invasive surgery

    Stephen McKinley;Animesh Garg;Siddarth Sen;Rishi Kapadia

  • TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning

    Adithyavairavan Murali;Animesh Garg;Sanjay Krishnan;Florian T. Pokorny

  • Tumor localization using automated palpation with Gaussian Process Adaptive Sampling

    Animesh Garg;Siddarth Sen;Rishi Kapadia;Yiming Jen

  • Large language models for chemistry robotics

    Unknown

  • IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

    Ajay Mandlekar;Fabio Ramos;Byron Boots;Silvio Savarese

  • Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter

    Michael Danielczuk;Andrey Kurenkov;Ashwin Balakrishna;Matthew Matl

  • DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

    Eric Heiden;Miles Macklin;Yashraj S Narang;Dieter Fox

  • Transition state clustering

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

  • Causal Discovery in Physical Systems from Videos

    Yunzhu Li;Antonio Torralba;Animashree Anandkumar;Dieter Fox

Frequent Co-Authors

Silvio Savarese
Silvio Savarese Stanford University
Ken Goldberg
Ken Goldberg University of California, Berkeley
Yuke Zhu
Yuke Zhu The University of Texas at Austin
Li Fei-Fei
Li Fei-Fei Stanford University
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Sachin Patil
Sachin Patil University of California, Berkeley
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Jeannette Bohg
Jeannette Bohg Stanford University
Hugo Larochelle
Hugo Larochelle Google (United States)
Byron Boots
Byron Boots University of Washington

External Links

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Best Scientists Citing Animesh Garg

Ken Goldberg

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Publications: 59

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Publications: 25

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Publications: 24

Leonidas J. Guibas

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Stanford University

Publications: 18

Jeannette Bohg

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Stanford University

Publications: 18

Dieter Fox

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Publications: 16

Danica Kragic

Danica Kragic

Royal Institute of Technology

Publications: 15

Jan Peters

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Technical University of Darmstadt

Publications: 15

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Johns Hopkins University

Publications: 14

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