Yuxuan Gao
Papers
1
Total Citations
2
H-Index
1
About
Yuxuan Gao is a rising researcher in intelligent robotics, with a primary focus on skill acquisition and reinforcement learning for industrial automation. Their most cited work, "Robotic Skill Acquisition in Peg-in-hole Assembly Tasks Based on Deep Reinforcement Learning" (2024), introduces a novel deep reinforcement learning (DRL) framework integrated with a PD force controller to enhance the efficiency and adaptability of robotic assembly strategies. This contribution addresses a critical challenge in precision manufacturing—enabling robots to learn complex, contact-rich tasks like peg-in-hole assembly with improved success rates and reduced training time. By bridging model-free learning with classical control, Gao’s approach offers a scalable solution for adaptive industrial robots. Though early in their career, with 2 citations to date, this work has already attracted attention for its practical relevance and methodological innovation. Gao’s research sits at the intersection of machine learning, control theory, and robotics, promising to advance autonomous skill transfer in manufacturing. Their work is particularly valuable for students and researchers exploring how deep reinforcement learning can be applied to real-world, high-precision robotic tasks.
Research Focus
Key Achievements
Top Papers
- 1