World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
9116
World Ranking
5739
National Ranking
101

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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