Yanzheng Lu
Papers
3
Total Citations
69
H-Index
3
About
Yanzheng Lu is a robotics researcher whose work sits at the intersection of intelligent control, human–robot interaction, and reinforcement learning. Their most influential contributions include a novel deep reinforcement learning method for dual-arm robots that tackles the long-standing challenge of mastering complex assembly tasks—work that has already garnered 32 citations since 2023. By enabling robots to learn intricate manipulation policies without requiring massive real-world data collection, Lu’s approach offers a practical path toward more dexterous and autonomous industrial robots. In parallel, Lu has pioneered non-invasive brain–robot interfaces, developing an online interaction method that uses single-channel EEG signals and a CNN with residual blocks to allow mobile robots to be controlled by thought alone—a breakthrough with 31 citations. Earlier foundational work includes the design and kinematic modeling of a teen-size humanoid robot, establishing closed-form inverse kinematics for biped walking based on the linear inverted pendulum model. Across these projects, Lu demonstrates a rare ability to bridge theoretical algorithm development with real-world robotic platforms, advancing both the intelligence and accessibility of robotic systems.
Research Focus
Key Achievements
Top Papers
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