Papers
8
Total Citations
58
H-Index
5
About
Wu-Te Yang is a robotics researcher whose work spans robot manipulation, sensor design, and soft robotics — fields at the frontier of intelligent automation. His early contributions focused on improving the precision and reliability of robotic systems: his 2016 optimization technique for identifying robot manipulator parameters under uncertainty (19 citations) addressed a fundamental challenge in factory automation, while his dual-arm manipulation strategies employed Kalman filter-based sensor fusion to achieve stable, coordinated object grasping across multiple publications. Building on this foundation in control systems and kinematics, Yang expanded his research into tactile sensing and soft robotics. His 2022 work on a multifunctional soft tactile sensor enhanced by machine learning (11 citations) demonstrated his ability to bridge hardware design with data-driven intelligence, enabling robots to handle delicate or irregularly shaped objects with greater sensitivity. More recently, he has tackled the complex challenges of modeling and controlling soft pneumatic actuators, introducing nonlinear data-driven frameworks and underactuated control strategies. With a growing body of work that integrates mechanical design, control theory, and machine learning, Yang's research is shaping the next generation of adaptable, human-safe robotic systems.
Research Focus
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