Yuanzhen Bi
Papers
2
Total Citations
38
H-Index
2
About
Yuanzhen Bi is a pioneering roboticist whose research centers on humanoid robotics, motion planning, and the integration of deep reinforcement learning with digital twin technologies. His most impactful work, "A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control" (2020, 35 citations), introduces a novel framework that combines twin synchro-control with reinforcement learning to enable humanoid robot arms to perform complex, multitasking operations with enhanced precision and adaptability. This contribution directly addresses key challenges in Industry 4.0 and the "Made in China 2025" initiative by advancing digital twin-driven automation. Bi also explores bipedal locomotion in his paper "Simulation of Improved Bipedal Running Based on Swing Leg Control and Whole-body Dynamics" (2021), where he refines biologically inspired deadbeat controllers to achieve robust, real-time running patterns with improved position adjustment. His work bridges theoretical control systems and practical robotic applications, offering scalable solutions for legged robots. With a growing citation footprint, Bi’s research continues to influence the development of agile, human-like robots capable of dynamic interaction in real-world environments.
Research Focus
Key Achievements
Top Papers
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