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About

Yasuhiro Wada is a pioneering roboticist and brain-computer interface (BCI) researcher whose work bridges dynamic optimization theory and neural signal processing. His most celebrated contribution is the development of a Kendama learning robot based on bi-directional theory (175 citations), which demonstrated how robots could acquire complex motor skills through motion optimization principles. This foundational work, along with his via-point time optimization algorithm for sequential trajectory formation (46 citations), established frameworks for teaching robots dexterous manipulation through demonstration and optimization. Wada's research has evolved to explore how brain function measurements can control robotic systems. His application of near-infrared spectroscopy (NIRS) for robot control (24 citations) and estimation of force motor commands for NIRS-based BMI (5 citations) represent significant advances in non-invasive BCI technology. He has also investigated EEG-based attention direction estimation using out-of-head sound localization (7 citations), expanding BCI applications beyond visual stimuli. His work on identifying motor imagery-related EEG features during motor execution (2020) continues to push the boundaries of neural decoding for robotic control. Through his integration of optimization theory, motor learning, and brain-machine interfaces, Wada has created a unique research program that advances both fundamental understanding of human motor control and practical applications in assistive robotics.