Quanyong Huang

Papers

Total Citations

H-Index

About

Quanyong Huang is a robotics researcher whose work focuses on the control and stabilization of bipedal locomotion, particularly for humanoid robots navigating challenging environments. His key contributions lie at the intersection of sensor-based control and machine learning. In his highly cited 2012 work, Huang introduced a walking control method that integrates Force Sensing Resistor (FSR) sensors with an inverted pendulum model, enabling more adaptive and stable gait patterns on flat surfaces. Building on this foundation, his 2015 paper tackled the more complex problem of biped walking on rough terrain by proposing a novel reinforcement learning framework. This approach used neural networks within an actor-critic architecture to handle the continuous state and action spaces inherent in dynamic walking, allowing the robot to learn stabilization policies without explicit terrain modeling. While his citation counts reflect a focused, early-career impact, Huang’s work is notable for its practical, sensor-driven approach to legged locomotion, bridging classical control theory with modern reinforcement learning to enhance robot adaptability in real-world, uneven environments.