Xiaoyi Long
Papers
1
Total Citations
10
H-Index
1
About
Xiaoyi Long is a researcher in robotics and intelligent control systems, with a focus on optimal control and reinforcement learning (RL) for autonomous robotic platforms. Their major contribution lies in developing efficient, online learning-based control strategies that reduce computational complexity while maintaining high performance. In their highly cited 2021 work, Long introduced a novel method for optimal tracking control of robotic systems using a single critic neural network (NN) for reinforcement learning, simplifying traditional dual-network architectures. This approach reformulates the robotic system into a state-space model, enabling real-time adaptation and improved tracking accuracy. The work has garnered 10 citations, reflecting its growing influence in the fields of adaptive control and robotics. Long’s research bridges the gap between theoretical RL algorithms and practical robotic applications, offering scalable solutions for dynamic environments. Their achievements highlight a commitment to advancing intelligent control systems, making their work essential reading for students and researchers interested in cutting-edge reinforcement learning techniques for robotics.
Research Focus
Key Achievements
Top Papers
- 1