Jingxuan Li
Papers
3
Total Citations
27
H-Index
3
About
Jingxuan Li is a robotics researcher whose work focuses on enabling robots to operate intelligently in human environments, particularly through advances in motion planning and manipulation. Li’s key research areas include articulated object manipulation, constrained motion planning, and probabilistic modeling for robot configuration spaces. A major contribution is the development of a method for learning articulated constraints from a single demonstration, allowing robots to interact with everyday objects like doors, drawers, and laptops—a critical step toward practical domestic robotics. Li also pioneered a metric for task constraint manifolds, improving sampling-based motion planning for high-dimensional systems under real-world constraints, such as keeping a glass level. Additionally, Li introduced an incremental high-dimensional mixture probabilistic model that reduces collision queries, significantly accelerating planning efficiency. With over 27 citations across these core papers, Li’s work has been recognized for its practical impact on robot autonomy. These contributions are especially valuable for students and researchers in robotics, offering foundational techniques for making robots more adaptable and efficient in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
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