People
森本 淳 Jun Morimoto

Jun MorimotoPrincipal Investigator

Professor

morimoto_at_i.kyoto-u.ac.jp

Research Interests

Reinforcement learning
Humanoid motor control
Human movement prediction
Assistive robots
Computational neuroscience

Biography

Jun Morimoto is Professor of Graduate School of Informatics, Kyoto University. He received his Ph.D. in information science from Nara Institute of Science and Technology (NAIST), Nara, Japan, in 2001. From 2001 to 2002, he was a postdoctoral fellow at the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA. He Jointed ATR in 2002. He also joined JST, ICORP from 2004 to 2009. From 2019 to 2022, he was a Team Leader of Man-Machine Collaboration Research Team, Robotics Project, RIKEN. He is also currently the Head of the Brain-Robot Interface Department at ATR Computational Neuroscience Laboratories.

Publications/Patents

Journal Papers

  • Matija Mavsar, Jun Morimoto, Ales Ude (2023), GAN-Based Semi-Supervised Training of LSTM Nets for Intention Recognition in Cooperative Tasks, IEEE Robotics and Automation Letters, in press.

  • Sunhwi Kang, Koji Ishihara, Norikazu Sugimoto, Jun Morimoto (2023), Curriculum-based humanoid robot identification using large-scale human motion database, Frontiers in Robotics and AI, doi.org/10.3389/frobt.2023.1282299, [Open Access]

  • Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara (2023), Learning to Shape by Grinding: Cutting-Surface-Aware Model-Based Reinforcement Learning, IEEE Robotics and Automation Letters, Volume: 8, Issue: 10, [Journal Page]

  • Asuka Takai, Qiushi Fu, Yuzuru Doibata, Giuseppe Lisi, Toshiki Tsuchiya, Keivan Mojtahedi, Toshinori Yoshioka, Mitsuo Kawato, Jun Morimoto*, Marco Santello* (2023), Learning acquisition of consistent leader–follower relationships depends on implicit haptic interactions, Scientific Reports, 13, Article number: 3476, [Open Access]

  • Yoko Takahashi et al. (2023), Robotized Knee-Ankle-Foot Orthosis-Assisted Gait Training on Genu Recurvatum during Gait in Patients with Chronic Stroke: A Feasibility Study and Case Report, Journal of Clinical Medicine, 12(2), 415, [Open Access]

  • Yuko Nakamura et al. (2023), Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders, Psychiatry and Clinical Neurosciences, doi.org/10.1111/pcn.13542,
    [Open Access]

  • Takuya Ishida, et al. (2023), Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets, Schizophrenia Bulletin, vol. 49 no. 4 pp. 933–943, [Open Access]

  • Shinya Chiyohara, Jun-ichiro Furukawa, Tomoyuki Noda, Jun Morimoto*, Hiroshi Imamizu (2023), Proprioceptive short-term memory in passive motor learning, Scientific Reports, 13, Article number: 20826, [Open Access]

  • Asuka Takai, Tatsuya Teramae, Tomoyuki Noda, Koji Ishihara, Jun-ichiro Furukawa, Hiroaki Fujimoto, Megumi Hatakenaka, Nobukazu Fujita, Akihiro Jino, Yuichi Hiramatsu, Ichiro Miyai, Jun Morimoto (2023), Development of split-force-controlled body weight support (SF-BWS) robot for gait rehabilitation, Frontiers in Human Neuroscience, doi.org/10.3389/fnhum.2023.1197380, [Open Access]

  • Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine, Eiji Uchibe, Jun Morimoto, Takamitsu Matsubara (2023), Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation, IEEE Robotics and Automation Letters, [Journal Page]

  • Matija Mavsar, Barry Ridge, Rok Pahic, Jun Morimoto, Ales Ude (2022), Simulation-Aided Handover Prediction From Video Using Recurrent Image-to-Motion Networks, IEEE Transactions on Neural Networks and Learning Systems, [Open Access]

  • Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto (2022), Deep learning, reinforcement learning, and world models, Neural Networks, Vol. 152, pp. 267-275, [Open Access]

  • Takeuchi H, Yahata N, Lisi G, Tsurumi K, Yoshihara Y, Kawada R, Murao T, Mizuta H, Yokomoto T, Miyagi T, Nakagami Y, Yoshioka T, Yoshimoto J, Kawato M, Murai T, Morimoto J, Takahashi H (2022), Development of a classifier for gambling disorder based on functional connections between brain regions, Psychiatry Clin Neurosci. 2022 Mar 13. doi: 10.1111/pcn.13350. [Open Access]

  • Jun-ichiro Furukawa, Shotaro Okajima, Qi An, Yuichi Nakamura, Jun Morimoto (2022), Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot, IEEE Robotics and Automation Letters, [Open Access]

  • Takeshi D. Itoh, Koji Ishihara, Jun Morimoto (2022), Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment, Neural Computation, [Open Access]

  • Asuka Takai, Giuseppe Lisi, Tomoyuki Noda, Tatsuya Teramae, Hiroshi Imamizu, Jun Morimoto (2021)
    Bayesian Estimation of Potential Performance Improvement Elicited by Robot-Guided Training
    Frontiers in Neurscience, fnins.2021.704402, [Open Access]

  • Tom Macpherson, Masayuki Matsumoto, Hiroaki Gomi, Jun Morimoto, Eiji Uchibe, Takatoshi Hikida (2021)
    Parallel and hierarchical neural mechanisms for adaptive and predictive behavioral control
    Neural Networks, Vol. 144, pp. 507-521, [Open Access]

  • Saori C. Tanaka, et al. (2021), A multi-site, multi-disorder resting-state magnetic resonance image database, Scientific Data, 8, Article number: 227 [Open Access]

  • Jun-ichiro Furukawa, Shinya Chiyohara, Tatsuya Teramae, Asuka Takai, Jun Morimoto (2021), A Collaborative Filtering Approach Toward Plug-and-Play Myoelectric Robot Control, IEEE Transactions on Human-Machine Systems, Vol. 51, Issue 5, 514-523. [Open Access]

  • Jun-ichiro Furukawa, Jun Morimoto (2021), Composing an Assistive Control Strategy Based on Linear Bellman Combination From Estimated User’s Motor Goal, IEEE Robotics and Automation Letters, Vol. 6, Issue: 2, pp. 1051 – 1058. [Open Access]

  • Prevoius papers can be found in [this page]

Conference Papers

[TBA]

Patents

[TBA]