Zhang Chen
Papers
2
Total Citations
17
H-Index
2
About
Zhang Chen is a robotics researcher specializing in the control and maneuverability of single-track two-wheeled (STTW) robots—agile, narrow-profile machines designed for challenging environments. His major contributions lie in applying reinforcement learning to solve complex dynamic tasks, most notably ramp jumping and high-speed navigation in confined terrains. Chen’s 2021 paper on continuous reinforcement learning for ramp jump control, with 15 citations, established a foundational method for enabling STTW robots to perform acrobatic maneuvers that were previously difficult to achieve. Building on this, his 2022 work on high maneuverability control in narrow terrain, though newer with 2 citations, addresses the critical challenge of fast, stable driving in mountainous or jungle settings. Chen’s research bridges the gap between theoretical reinforcement learning and practical robotic locomotion, offering solutions for energy-efficient, small-footprint robots. His work is particularly notable for its focus on real-world deployment, pushing the boundaries of what two-wheeled robots can accomplish in unstructured environments. For students and researchers, Chen’s studies represent a compelling intersection of control theory, machine learning, and field robotics.
Research Focus
Key Achievements
Top Papers
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- 2