Chengyun Wang
Papers
1
Total Citations
2
H-Index
1
About
Chengyun Wang is a researcher focused on advancing human-robot interaction through biologically inspired motion planning. Their primary research area centers on developing humanoid trajectory planning algorithms that enable robots to move with greater naturalness and efficiency, particularly in collaborative settings. Wang’s major contribution lies in the application of the minimum-jerk model—a principle from human motor control—to robot arm movements, allowing for smoother, more predictable trajectories that mirror human motion. Their most-cited work, "Humanoid Trajectory Planning for Robot Based on Minimum-Jerk Model of Human Arm" (2022), specifically addresses the pickup and delivery motions critical to human-robot interaction, establishing a quantitative relationship between peak velocity and motion distance in human reaching movements. This foundational study, with 2 citations, provides a framework for designing robots that can anticipate and adapt to human partners, enhancing safety and fluidity in shared workspaces. Wang’s research bridges neuroscience and robotics, offering practical insights for creating more intuitive and collaborative machines.
Research Focus
Key Achievements
Top Papers
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