Papers
4
Total Citations
15
H-Index
2
About
Shuhui Yang is a robotics researcher specializing in legged robot locomotion, autonomous navigation, and intelligent control systems. Their work sits at the intersection of deep reinforcement learning, model predictive control, and multi-sensor fusion, with a particular focus on advancing the capabilities of biped and quadruped robots in complex real-world environments. Among Yang's most notable contributions is a 2022 study introducing a deep reinforcement learning-based model predictive controller for biped robots, which innovatively treats the swinging leg as a disturbance within a modified single rigid body model — a practical refinement that has attracted 6 citations. Their research on quadruped robot locomotion further demonstrates a commitment to replacing tedious manual control design with learned, adaptive behaviors through reinforcement learning frameworks. More recently, Yang has made significant strides in robotic perception and mapping, developing SLI-SLAM, a sophisticated multi-sensor fusion framework combining stereo cameras, LiDAR, and IMU data via factor graph optimization to enable reliable autonomous navigation in challenging environments. Complementary work on MSOT-Q integrates object detection with SLAM for richer environmental awareness. Collectively, Yang's research represents a coherent and growing body of work pushing legged robots toward greater autonomy and real-world deployability.
Research Focus
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Top Papers
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