Papers
9
Total Citations
70
H-Index
5
About
Satoshi Satoh is a leading figure in the field of robotic locomotion, specializing in the development of optimal gait generation for legged robots. His research centers on applying advanced control theory—particularly iterative learning control (ILC) and iterative feedback tuning (IFT)—to Hamiltonian systems, leveraging their unique property of variational symmetry to design efficient, stable walking and running patterns. Satoh’s most influential work, such as his 2008 paper on biped gait generation via ILC with discrete state transitions (17 citations) and his 2006 framework for passive running using variational symmetry (16 citations), demonstrates a pioneering approach to learning optimal trajectories that minimize energy consumption while ensuring dynamic stability. He has extended these methods to one-legged hopping robots and bipedal systems with knees and torsos, as seen in his 2018 study on trajectory learning for complex bipeds. With a cumulative citation count exceeding 70 across his key publications, Satoh’s contributions are foundational for researchers seeking to bridge control theory and practical robotics. His unified learning optimal control framework, integrating ILC and IFT for Hamiltonian systems, represents a notable achievement, offering a systematic pathway from simulation to real-world robotic gait synthesis.
Research Focus
Key Achievements
Top Papers
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- 2Gait Generation for Passive Running via Iterative Learning Control16 citations · 2006
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