Xujie Shen
Papers
1
Total Citations
5
H-Index
1
About
Xujie Shen is a rising researcher at the forefront of embodied artificial intelligence and robotics, with a primary focus on advancing neural motion planning. His most notable contribution is the development of PC-Planner, a physics-constrained self-supervised learning framework that introduces a shape-aware distance function for robust motion planning. This work, published in 2024 and already garnering 5 citations, addresses the critical challenge of high-dimensional complexities that traditional motion planning methods struggle with. By integrating physics-informed constraints into neural planners, Shen's approach enables more efficient and reliable navigation for robotic systems, directly tackling the burgeoning demands of embodied AI. His research sits at the intersection of machine learning, physics simulation, and robotics, offering a novel paradigm that reduces reliance on expensive labeled data while improving planning robustness. As an emerging voice in the field, Shen's work is paving the way for more intelligent, adaptive robots capable of operating in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1