Xiaohu Yu
Papers
1
Total Citations
2
H-Index
1
About
Xiaohu Yu is a leading researcher at the intersection of robotics and construction automation, with a primary focus on deep reinforcement learning for dexterous manipulation. His most-cited work, “Teaching Robot End Effectors to Grasp Construction Tools Based on Deep Reinforcement Learning” (2025, 2 citations), tackles a critical bottleneck in construction robotics: enabling robots to handle irregular, delicate objects like hammers, scaffolding, and drills. Yu’s major contribution lies in developing RL-based motor skill acquisition for end effectors, moving beyond rigid, pre-programmed grasps to adaptive, real-time control. This work addresses the nuanced challenge of automating skilled labor tasks that have long resisted robotic intervention. While his citation count is still growing, the novelty of his approach—combining deep RL with construction-specific tool manipulation—positions him at the forefront of a rapidly evolving field. Yu’s research has the potential to reshape onsite construction, reducing reliance on human workers for hazardous or repetitive tasks. His achievements include pioneering a framework that could generalize to other delicate object manipulation domains, signaling a promising trajectory for both robotics and construction engineering.
Research Focus
Key Achievements
Top Papers
- 1