Papers
29
Total Citations
669
H-Index
12
About
Tatsuya Teramae is a pioneering robotics researcher whose work sits at the intersection of exoskeleton design, human-robot interaction, and intelligent control systems. His research has made substantial contributions to rehabilitation robotics and assistive technologies, addressing one of the most pressing challenges of our aging global population. Teramae is perhaps best known for his development of EMG-based control frameworks, most notably his 2017 model predictive control approach for "assist-as-needed" rehabilitation, which has garnered over 146 citations and represents a landmark contribution to adaptive robotic therapy. His parallel work on exoskeleton hardware is equally impressive, spanning pneumatic-electric hybrid actuation systems and variable stiffness ankle joints for balance control — the latter accumulating 86 citations for its elegant application to bipedal robots. What distinguishes Teramae's work is his integration of machine learning into assistive robotics. Through reinforcement learning and multi-task learning frameworks, he has enabled exoskeletons to adapt assistive strategies directly from physical human-robot interactions, reducing reliance on pre-programmed models. His research on biosignal sensor failure detection further reflects a commitment to real-world reliability. With over 520 cumulative citations across his major works, Teramae has established himself as a formative voice in wearable robotics research.
Research Focus
Key Achievements
Top Papers
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- 7An electromyogram based force control coordinated in assistive interaction21 citations · 2013
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