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Man-Machine Collaboration
Research Team

Research Summary

We aim to develop an invisible robot that detect human intentions and support them in the secretly without impairing their self-agency. Through this development, we will elucidate the principle of human-machine collaboration that can maximize human's ability and demonstrate it to actual robot system.

Main Research Fields
  • Assistive Robot
Keywords
  • Exoskeleton robot
  • Motor control
  • Motion estimation & prediction
  • Machine learning
  • Bio-signal analysis
Research them
  • Development of tailor-made adaptive motion support system
  • Real-time motion prediction for assist in exquisite ways
  • Human motion estimation based on bio-signal

Takashi Minato

Takashi Minato

History

2001
Japan Science and Technology Agency (JST)
2002
Osaka University
2006
Japan Science and Technology Agency (JST)
2011
Advanced Telecommunications Research Institute International (ATR)
2020
RIKEN

Award

2016
ATR Prize for excellent study
2020
ATR Prize for excellent study

Members

Jun Morimoto
Senior Visiting Scientist
Jun-ichiro Furukawa
Research Scientist
Takahide Ito
Special technical staff
Akihide Inano
Senior Technical Staff
Ren Kai
Student Trainee

Former member

Ilham Julian
Student Trainee (2022/01-2022/03)
Nag Aneek
Research Intern (2022/08-2022/09)
Elena Basei
Research Intern (2022/10-2022/12 2023/7-2023/09)

Research results

  • J. Furukawa, S. Okajima, Q. An, Y. Nakamura and J. Morimoto, "Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot," in IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3890-3897, April 2022, doi: 10.1109/LRA.2022.3148799.
  • J. Furukawa, S. Chiyohara, T. Teramae, A. Takai and J. Morimoto, “A Collaborative Filtering Approach Toward Plug-and-Play Myoelectric Robot Control,” in IEEE Transactions on Human-Machine Systems, vol. 51, no. 5, pp. 514-523, Oct. 2021, doi: 10.1109/THMS.2021.3098115.
  • J. Furukawa, J. Morimoto, “Composing an Assistive Control Strategy Based on Linear Bellman Combination From Estimated User’s Motor Goal”, IEEE Robotics and Automation Letters, vol. 6, No. 2, pp. 1051-1058, 2021
  • T. Teramae, T. Matsubara, T. Noda, J. Morimoto, “Quaternion-Based Trajectory Optimization of Human Postures for Inducing Target Muscle Activation Patterns”, IEEE Robotics and Automation Letters, Vol. 5, No. 4, pp. 6607-6614, 2020. (Joint Research with ATR)
A Collaborative Filtering Approach using other user information toward EMG-Based robot control with less calibration effort Improving human motor performance with exoskeleton robot

Selected Publications

  1. Tatsuya Teramae, Takamitsu Matsubara, Tomoyuki Noda, Jun Morimoto.:
    "Quaternion-Based Trajectory Optimization of Human Postures for Inducing Target Muscle Activation Patterns"
    IEEE Robotics and Automation Letters, Vol. 5, Issue: 4, pp. 6607-6614, 2020
  2. Shinya Chiyohara, Jun-ichiro Furukawa, Tomoyuki Noda, Jun Morimoto and Hiroshi Imamizu.:
    "Passive Training with Upper Extremity Exoskeleton Robot Affects Proprioceptive Acuity and Performance of Motor Learning"
    Scientific Reports, 10, Article number; 11820, 2020
  3. Masashi Hamaya, Takamitsu Matsubara, Tatsuya Teramae, Tomoyuki Noda, and Jun Morimoto.:
    "Design of Physical User-Robot Interactions for Model Identification of Soft Actuators on Exoskeleton Robots"
    International Journal of Robotics Research, first published online: June 6, 2019
  4. Barkan Ugurlu, Paolo Formi, Corinne Doppmann, Emre Sariyildiz, and Jun Morimoto.:
    "Stable Control of Force, Position, and Stiffness for Robot Joints Powered via Pneumatic Muscles"
    IEEE Transactions on Industrial Informatics, Vol. 15, Issue 12, pp. 6270-6279
  5. Tadej Petric, Luka Peternel, Jun Morimoto, and Jan Babic.:
    "Assistive Arm-Exoskeleton Control Based on Human Muscular Manipulability
    Frontiers in Neurorobotics, fnbot.2019.00030, 2019
  6. Tatsuya Teramae, Koji Ishihara, Jan Babic, Jun Morimoto, and Erhan Oztop.:
    "Human-in-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots"
    Frontiers in Neurorobotics, fnbot.2018.00071
  7. Tatsuya Teramae, Tomoyuki Noda, and Jun Morimoto.:
    "EMG-Based Model Predictive Control for Physical Human-Robot Interaction: Application for Assist-As-Needed Control"
    IEEE Robotics and Automation Letters, Vol. 3, issue 1, pp.210-217, 2018
  8. Masashi Hamaya, Takamitsu Matsubara, Tomoyuki Noda, Tatsuya Teramae, and Jun Morimoto.:
    "Learning assistive strategies for exoskeleton robots from user-robot physical interaction"
    Pattern Recognition Letters, Vol. 99, pp. 67-76, 2017
  9. Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, and Jun Morimoto.:
    "Human movement modeling to detect bio-signal sensor failures for myoelectric assistive robot control"
    IEEE Transactions on Robotics, Vol. 33, Issue 4, pp. 846-857, 2017
  10. Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, and Jun Morimoto.:
    "An EMG-Driven Weight Support System with Pneumatic Artificial Muscles"
    IEEE Systems Journal, Vol. 10, No. 3, pp. 1026-1034, 2016

Links

Man-Machine Collaboration Research Team(RIKEN)

Contact Information

takashi.minato [at] riken.jp

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