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About

Koichi Okuda is a pioneering researcher in the field of bio-inspired robotics, with a particular focus on the locomotion and recovery mechanisms of articulated, limbless mobile robots. His most significant contributions center on the development and gait analysis of inchworm-type robots, which move through narrow, constrained environments using vertically undulatory motion of their entire body. Okuda’s foundational work, including his 1998 and 1999 studies on inchworm gaits (cited 8 and 7 times respectively), established core principles for navigating tight spaces without legs or wheels. He further advanced the field by addressing critical failure modes, such as in his 2004 study on returning motion from a sideways fall, demonstrating how a six-segment robot could self-right after overturning. Notably, Okuda also integrated artificial intelligence into robotics, applying Fuzzy ART neural networks for state-space segmentation in reinforcement learning. His 2005 work showed that by incrementally expanding the state space and transferring value functions from similar existing units, learning speed improved while conserving hardware resources—a contribution validated through both simulation and experiment. Okuda’s research remains influential for students and engineers working on minimalistic, adaptive, and resilient robotic systems.