World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
9116
World Ranking
5741
National Ranking
101

Jun Morimoto publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Jun Morimoto sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 215 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Jun Morimoto D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Jun Morimoto sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 44 D-Index — 42nd percentile

42% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Jun Morimoto is affiliated with the Advanced Telecommunications Research Institute International in Japan. Their research spans multiple disciplines, primarily focused on engineering, medicine, and neuroscience. Within these fields, they have contributed significantly to specialized subfields including biomedical engineering, cognitive neuroscience, control and systems engineering, computer vision and pattern recognition, and neurology.

The scientist's body of work centers on several main topics, reflecting a cross-disciplinary approach. These include robot manipulation and learning, muscle activation and electromyography studies, stroke rehabilitation and recovery, prosthetics and rehabilitation robotics, motor control and adaptation, neural dynamics and brain function, and human pose and action recognition.

Jun Morimoto has published extensively in various reputable venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • Spinal Surgery
  • Scientific Reports
  • Neural Networks

Their recent papers illustrate a diverse range of research interests and collaboration. Selected publications include:

  • "Deep learning, reinforcement learning, and world models" (2022), Neural Networks
  • "A multi-site, multi-disorder resting-state magnetic resonance image database" (2021), Scientific Data
  • "Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants" (2020), Scientific Reports
  • "Training of deep neural networks for the generation of dynamic movement primitives" (2020), Neural Networks
  • "Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity" (2020), Schizophrenia Bulletin

Collaboration is a key aspect of Morimoto's research, with frequent co-authors including:

  • Tomoyuki Noda
  • Mitsuo Kawato
  • Takamitsu Matsubara
  • Giuseppe Lisi
  • Satoshi Yamamori

Best Publications

  • Learning from demonstration and adaptation of biped locomotion

    Jun Nakanishi;Jun Morimoto;Gen Endo;Gordon Cheng

  • Deep learning, reinforcement learning, and world models

    Unknown

  • Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives

    Aleš Ude;Andrej Gams;Tamim Asfour;Jun Morimoto

  • Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning

    Jun Morimoto;Kenji Doya

  • A small number of abnormal brain connections predicts adult autism spectrum disorder

    Noriaki Yahata;Jun Morimoto;Ryuichiro Hashimoto;Giuseppe Lisi

  • CB: A Humanoid Research Platform for Exploring NeuroScience

    G. Cheng;Sang-Ho Hyon;J. Morimoto;A. Ude

  • Robust Reinforcement Learning

    Jun Morimoto;Kenji Doya

  • Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot

    Gen Endo;Jun Morimoto;Takamitsu Matsubara;Jun Nakanishi

  • Orientation in Cartesian space dynamic movement primitives

    Ales Ude;Bojan Nemec;Tadej Petric;Jun Morimoto

  • Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

    Ayumu Yamashita;Noriaki Yahata;Noriaki Yahata;Takashi Itahashi;Giuseppe Lisi

  • ROBOT APPARATUS AND A METHOD FOR CONTROLLING THE POSTURE OF A ROBOT FOR STABILIZING THE POSTURE OF THE ROBOT ACCORDING TO PERIODICAL MOTION

    Cheng Gordon;Endo Gen;Kawato Mitsuo;Morimoto Jun

  • Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric Interface

    Takamitsu Matsubara;Jun Morimoto

  • Experimental Studies of a Neural Oscillator for Biped Locomotion with QRIO

    Gen Endo;Jun Nakanishi;Jun Morimoto;G. Cheng

  • Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation.

    Luka Peternel;Tomoyuki Noda;Tadej Petrič;Aleš Ude

  • A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

    J. Morimoto;G. Endo;J. Nakanishi;G. Cheng

  • An empirical exploration of a neural oscillator for biped locomotion control

    G. Endo;J. Morimoto;J. Nakanishi;G. Cheng

  • EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control

    Tatsuya Teramae;Tomoyuki Noda;Jun Morimoto

  • Learning CPG-based biped locomotion with a policy gradient method

    Takamitsu Matsubara;Jun Morimoto;Jun Nakanishi;Masa-aki Sato

  • A multi-site, multi-disorder resting-state magnetic resonance image database

    Saori C Tanaka;Ayumu Yamashita;Noriaki Yahata;Takashi Itahashi

  • A simple reinforcement learning algorithm for biped walking

    J. Morimoto;G. Cheng;C.G. Atkeson;G. Zeglin

  • Minimax differential dynamic programming: application to a biped walking robot

    J. Morimioto;G. Zeglin;C.G. Atkeson

  • On-line motion synthesis and adaptation using a trajectory database

    Denis Forte;Andrej Gams;Jun Morimoto;Aleš Ude

Frequent Co-Authors

Gordon Cheng
Gordon Cheng Technical University of Munich
Mitsuo Kawato
Mitsuo Kawato Advanced Telecommunications Research Institute International
Gen Endo
Gen Endo Tokyo Institute of Technology
Noriaki Yahata
Noriaki Yahata National Institutes for Quantum and Radiological Science and Technology
Ales Ude
Ales Ude Jožef Stefan Institute
Kenji Doya
Kenji Doya Okinawa Institute of Science and Technology
Kiyoto Kasai
Kiyoto Kasai University of Tokyo
Hidehiko Takahashi
Hidehiko Takahashi Tokyo Medical and Dental University
Nobumasa Kato
Nobumasa Kato Showa University
Christopher G. Atkeson
Christopher G. Atkeson Carnegie Mellon University

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