Tatsuya Teramae

Advanced Telecommunications Research Institute International, Interface (United States)

Papers

29

Total Citations

669

H-Index

12

About

No biography available yet.

Research Focus

Artificial intelligence25 · 643 citations
Computer science25 · 643 citations
Engineering19 · 616 citations
Robot22 · 542 citations
Simulation18 · 448 citations
Exoskeleton17 · 444 citations
Control (management)11 · 401 citations
Control theory (sociology)10 · 389 citations
Torque9 · 381 citations
Physical medicine and rehabilitation14 · 368 citations
Control engineering8 · 324 citations
Actuator11 · 280 citations

Key Achievements

12
H-Index
29
Papers
669
Total Citations
23
Avg Citations/Paper
🏆 Most Cited Paper
EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control
146 citations · 2017
📈 Most Prolific Year: 2017 (5 Papers)
🤝 Key Collaborators: 56
🏛 Institutions: Advanced Telecommunications Research Institute International, Interface (United States)

Top Papers

  1. 1
    EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control
    146 citations · 2017
  2. 2
    Variable Ankle Stiffness Improves Balance Control: Experiments on a Bipedal Exoskeleton
    86 citations · 2015
  3. 3
    Learning assistive strategies for exoskeleton robots from user-robot physical interaction
    79 citations · 2017
  4. 4
    Development of an upper limb exoskeleton powered via pneumatic electric hybrid actuators with bowden cable
    71 citations · 2014
  5. 5
    Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots
    34 citations · 2019
  6. 6
    Human Movement Modeling to Detect Biosignal Sensor Failures for Myoelectric Assistive Robot Control
    31 citations · 2017
  7. 7
    An electromyogram based force control coordinated in assistive interaction
    21 citations · 2013
  8. 8
    Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach
    19 citations · 2016
  9. 9
    Optimal control approach for pneumatic artificial muscle with using pressure-force conversion model
    18 citations · 2014
  10. 10
    Learning task-parametrized assistive strategies for exoskeleton robots by multi-task reinforcement learning
    17 citations · 2017

Key Collaborators

TNTomoyuki Noda27 shared papersJMJun Morimoto26 shared papersTMTakamitsu Matsubara10 shared papersJFJun-ichiro Furukawa8 shared papersMHMasashi Hamaya8 shared papersMKMichiyuki Kawakami4 shared papersATAsuka Takai3 shared papersBUBarkan Uğurlu3 shared papersYYYuka Yamada2 shared papersCDCorinne Doppmann2 shared papersYHYuichiro Hosoi2 shared papersTTTetsuya Tsuji2 shared papersTKTakayuki Kamimoto2 shared papersSHSang-Ho Hyon2 shared papersKOKohei Okuyama2 shared papersKIKoji Ishihara2 shared papersJOJan Oskar Brinker1 shared papersSCShinya Chiyohara1 shared papersNMNaoto Mano1 shared papersTOTsukasa Ogasawarsa1 shared papers
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