Papers
9
Total Citations
143
H-Index
6
About
Jiexin Xie is a leading researcher at the intersection of robotics, artificial intelligence, and human-robot interaction, with a primary focus on advancing intelligent systems for healthcare and assistive technologies. Their work centers on three key areas: deep reinforcement learning for robotic trajectory planning, multi-modal surgical trajectory segmentation, and dual-arm coordination for nursing robots. Xie’s most impactful contribution is a 2019 paper on deep reinforcement learning with optimized reward functions for robotic trajectory planning (75 citations), which introduced novel dense reward functions that significantly improved the efficiency of robot navigation in unstructured environments. This work has been foundational for rehabilitation robotics and autonomous manipulation. Xie has also pioneered unsupervised methods for surgical trajectory segmentation using video and kinematic data, enabling faster, more accurate surgical skill assessment and robot learning in minimally invasive surgery. More recently, Xie has explored large language model-driven dual-arm collaboration frameworks for nursing robots and 3D whole-body human pose forecasting with grasping objects, pushing the boundaries of humanoid robot dexterity. With over 140 total citations and a growing portfolio of high-impact publications, Xie is recognized for translating complex AI techniques into practical robotic solutions that enhance patient care and surgical outcomes.
Research Focus
Key Achievements
Top Papers
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- 6Forecasting of 3D Whole-Body Human Poses with Grasping Objects6 citations · 2024
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