Fengjun Yang
Papers
1
Total Citations
2
H-Index
1
About
Fengjun Yang is a robotics researcher whose work focuses on bridging the gap between trajectory generation and control for complex, underactuated robotic systems. His primary research areas include dynamics-aware motion planning, nonlinear control, and data-driven methods for robotic manipulation and locomotion. Yang’s most notable contribution is his pioneering data-driven approach to synthesizing trajectories that inherently account for system dynamics, enabling more agile and efficient motion in robots that cannot be fully controlled at all times. His 2023 paper, "A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems," introduces a framework that integrates simplified models with real-world data to generate feasible, trackable trajectories—a critical advance for systems like quadrotors, bipedal robots, and manipulators. Though early in his career, Yang’s work has already garnered attention for its practical impact on layered control architectures, offering a systematic way to improve performance without sacrificing computational tractability. His research promises to make underactuated robots more reliable and versatile in real-world environments, from search-and-rescue to autonomous manufacturing.
Research Focus
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Top Papers
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