Xiang Xie
Papers
4
Total Citations
23
H-Index
2
About
Xiang Xie is a researcher at the forefront of robotics and intelligent control, with a primary focus on reinforcement learning for robotic locomotion and surgical robotics. His most impactful work centers on developing efficient algorithms for real-world robotic applications. Notably, his 2021 paper on a portable accelerator for Proximal Policy Optimization (PPO) for robots, which has garnered 11 citations, addresses a critical bottleneck in deploying reinforcement learning on physical systems by optimizing the three-neural-network architecture for training and inference. Earlier, in 2020, Xie tackled the challenge of bipedal robot walking, proposing a framework that combines deep reinforcement learning with traditional control to improve convergence and training efficiency—work that has earned 8 citations. Beyond locomotion, Xie has made contributions to medical robotics, including a registration method for Total Knee Arthroplasty surgical robots (2022) and an optical tracker-based registration technique for robot-assisted needle insertion surgeries (2017), both aimed at enhancing surgical precision. His work bridges the gap between theoretical reinforcement learning and practical robotic systems, with a clear impact on both industrial automation and healthcare.
Research Focus
Key Achievements
Top Papers
- 1A Portable Accelerator of Proximal Policy Optimization for Robots11 citations · 2021
- 2Motion Sequence Learning for Robot Walking Based on Pose optimization8 citations · 2020
- 3A Registration Method for Total Knee Arthroplasty Surgical Robot2 citations · 2022
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