Jialing Huang
Papers
1
Total Citations
2
H-Index
1
About
Jialing Huang is a researcher in robotics and artificial intelligence, with a primary focus on humanoid robot locomotion and reinforcement learning. Their most notable contribution is a pioneering model-free reinforcement learning method for gait control, detailed in the 2018 paper "A Reinforcement Learning Method for Humanoid Robot Walking." This work innovatively combines Q-learning with Radial Basis Function (RBF) Networks to address the challenge of continuous state and action spaces in robotic control, enabling more adaptive and efficient walking behaviors. Although the paper has garnered 2 citations, it represents a foundational step in bridging reinforcement learning and humanoid robotics. Huang’s research is particularly valuable for students and researchers exploring autonomous robot learning, as it demonstrates how neural network approximations can enhance traditional RL algorithms. Their work contributes to the broader goal of creating more versatile, self-learning robots capable of navigating complex environments.
Research Focus
Key Achievements
Top Papers
- 1A Reinforcement Learning Method for Humanoid Robot Walking2 citations · 2018