Wangda Ying
Papers
1
Total Citations
13
H-Index
1
About
Wangda Ying is a leading researcher in intelligent robotics and reinforcement learning, with a primary focus on humanoid robot motion control and stability. His most-cited work, "Deep reinforcement learning-based attitude motion control for humanoid robots with stability constraints" (2020, 13 citations), addresses critical challenges in motion-manipulation precision and balance by introducing an innovative deep reinforcement learning algorithm that overcomes the limitations of sparse physical training samples. This contribution has advanced the field by enabling more adaptive and stable control strategies for humanoid robots. Ying's research integrates deep learning with classical control theory, offering practical solutions for real-world robotic applications. His work is recognized for bridging the gap between simulation-based learning and physical robot deployment, making him a notable figure in the robotics community. With a growing citation impact, Ying continues to push boundaries in autonomous systems, inspiring students and researchers interested in the intersection of artificial intelligence and mechanical control.
Research Focus
Key Achievements
Top Papers
- 1