Yufeng Xiong

University of Freiburg, University of Nottingham Ningbo China

Papers

Total Citations

127

H-Index

About

Yufeng Xiong is a robotics researcher whose work bridges the critical gap between simulation and reality for intelligent control systems. His primary research areas include domain adaptation for visual control, deep reinforcement learning (DRL), and human-robot interaction, with a particular focus on enabling robots to transfer skills learned in simulation to complex real-world environments. Xiong’s most impactful contribution is the “VR-Goggles for Robots” framework, which tackles the reality gap by transforming real-world visual data into simulated-like representations, allowing DRL policies to be deployed directly on physical robots without extensive retraining. This work, published in 2019, has garnered 107 citations, reflecting its significance in advancing practical robot autonomy. Additionally, Xiong has explored vision-based motion control using depth maps for wheeled mobile robots and, more recently, investigated gait recognition for lower-limb exoskeletons by fusing sEMG and IMU signals—a step toward seamless human-robot collaboration. His research not only pushes the boundaries of sim-to-real transfer but also contributes to assistive robotics, making autonomous systems more adaptable and accessible in real-world settings.