Weining Zhang
Papers
1
Total Citations
31
H-Index
1
About
Weining Zhang is a leading researcher in humanoid robotics, with a primary focus on whole-body locomotion and reinforcement learning (RL). His most-cited work, "Whole-body Humanoid Robot Locomotion with Human Reference" (2024, 31 citations), addresses a critical challenge in the field: enabling humanoid robots to perform complex, dynamic movements by leveraging human motion data. Zhang’s major contribution lies in developing RL frameworks that simplify reward function design and streamline the training of full-body controllers, overcoming the traditional difficulties of high-dimensional control and stability. By integrating human reference trajectories, his approach allows robots to achieve more natural, efficient, and robust locomotion—paving the way for real-world applications in service, manufacturing, and disaster response. Though his citation count is still growing, the work has already garnered attention for its practical impact on reducing the engineering burden in humanoid control. Zhang’s research continues to push the boundaries of how robots learn from human demonstrations, making him a rising voice in the intersection of machine learning and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1Whole-body Humanoid Robot Locomotion with Human Reference31 citations · 2024