Yongjie Feng

Papers

Total Citations

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About

Yongjie Feng’s research centers on human-robot interaction, with a particular focus on intuitive, vision-based control systems for robotic manipulation. In his most-cited work, “Kinect-Based Hand Tracking for First-Person-Perspective Robotic Arm Teleoperation” (2018), Feng developed a novel approach that uses depth-sensing technology to capture hand position vectors, enabling natural, real-time control of a robotic arm from a first-person perspective. This contribution addresses a critical challenge in teleoperation—bridging the gap between human intent and machine action—by eliminating the need for complex wearable sensors or controllers. Although his citation count is currently modest (3 citations for this paper), the work represents a foundational step toward more accessible and immersive human-machine interfaces. Feng’s approach leverages virtual robotic arm simulations to validate tracking accuracy, demonstrating a practical pathway for applications in remote surgery, hazardous environment exploration, and assistive robotics. His research underscores a commitment to making robotic systems more responsive and user-friendly, with potential implications for both industrial automation and rehabilitation technologies. As the field of teleoperation continues to grow, Feng’s early work offers a promising template for integrating consumer-grade sensors into advanced robotic control systems.