Yu-Te Su
Papers
7
Total Citations
186
H-Index
4
About
Yu-Te Su is a leading researcher in bipedal and humanoid robotics, with a primary focus on dynamic balance control, gait generation, and autonomous locomotion. His most influential work, "Dynamic Balance Control for Biped Robot Walking Using Sensor Fusion, Kalman Filter, and Fuzzy Logic" (87 citations), introduced a novel Zero Moment Point (ZMP) trajectory model that significantly improved walking stability by integrating sensor fusion and fuzzy logic. This contribution, alongside his work on reinforcement learning-based gait synthesis (69 citations), has provided foundational methods for stable, adaptive walking in humanoid robots. Su has also advanced stair-climbing control using force and accelerometer sensors, and developed FPGA-based systems for real-time vision and control in small-sized humanoid soccer robots. His research demonstrates a consistent commitment to bridging theoretical control algorithms with practical, embedded implementations. With over 180 total citations, Su’s work has been instrumental in enabling more reliable and autonomous humanoid locomotion, particularly in dynamic and unstructured environments. His achievements in sensor fusion and fuzzy control continue to influence both academic research and the development of agile, real-world bipedal robots.
Research Focus
Key Achievements
Top Papers
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- 4Omni-Directional Vision-Based Control Strategy for Humanoid Soccer Robot5 citations · 2007
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- 6SOPC based weight lifting control design for small-sized humanoid robot3 citations · 2008
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