Yueqiang Dong
Papers
2
Total Citations
68
H-Index
2
About
Yueqiang Dong is a robotics researcher whose work bridges reinforcement learning, evolutionary optimization, and human-robot interaction. His most cited paper, "Reinforcement Learning With Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion" (2022, 55 citations), introduces a novel framework that combines RL with evolutionary trajectory generation to overcome reward sparsity and complex nonlinear dynamics in quadrupedal robots. This approach significantly reduces manual engineering for locomotion controllers. Dong also advances social robotics through his work on "Proactive Interaction Framework for Intelligent Social Receptionist Robots" (2021, 13 citations), which develops vision-based systems enabling receptionist robots to actively greet and assist people, improving user acceptance and satisfaction. His research addresses key challenges in both locomotion and social robotics, demonstrating impact in creating more autonomous and intuitive robotic systems. With a focus on practical, generalizable solutions, Dong's contributions are shaping how robots learn to move and interact in real-world environments, making him a notable figure in contemporary robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Proactive Interaction Framework for Intelligent Social Receptionist Robots13 citations · 2021