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About

Hiroaki Gomi is a distinguished researcher whose work spans adaptive motor control, neural learning systems, and sensorimotor perception. Best known for his pioneering contributions to robot learning and neural network-based control, Gomi helped establish foundational frameworks for feedback-error-learning, a scheme enabling neural networks to adaptively compensate for nonlinearities in controlled systems — work that has garnered nearly 90 citations and continues to influence robotics and computational neuroscience alike. His celebrated Kendama learning robot series, accumulating over 175 citations, demonstrated how optimization principles and bidirectional learning theories could enable machines to acquire complex dynamic skills through observation and practice. In recent years, Gomi has expanded his focus toward understanding how the brain constructs spatial self-awareness and world models, contributing influential theoretical work on hierarchical and parallel neural mechanisms for adaptive behavioral control. His explorations of tactile and proprioceptive signal integration — including studies on the rubber hand illusion and haptic self-perception — reveal a deep commitment to uncovering how sensorimotor signals shape bodily awareness. With a body of work bridging robotics, cognitive neuroscience, and artificial intelligence, Gomi represents a rare intellectual thread connecting machine learning's early promise to contemporary questions about perception, embodiment, and brain-inspired intelligence.