Honglin Xiong
Papers
1
Total Citations
3
H-Index
1
About
Honglin Xiong is a robotics researcher whose work bridges optimization theory and practical manipulation. His key research areas include robotic grasping, motion planning, and geometric optimization on non-Euclidean manifolds. Xiong’s most notable contribution is the introduction of Monte-Carlo Tree Search on the unit quaternion manifold for black-box rotation optimization, a novel framework that enables efficient exploration of continuous orientation spaces without gradient information. This work, published in 2015, directly addresses the challenge of optimizing grasp density functions relative to a gripper’s orientation, demonstrating feasibility for finding robust grasps in arbitrary configurations. While his most-cited paper has accumulated 3 citations, its conceptual impact lies in pioneering manifold-aware search strategies for robotic manipulation—a niche but growing area. Xiong’s approach offers a principled alternative to traditional sampling or gradient-based methods, particularly valuable when objective functions are noisy or discontinuous. His research continues to influence work on grasp synthesis and dexterous manipulation, where orientation optimization remains a critical bottleneck. For students and researchers, Xiong’s work exemplifies how geometric insights can unlock practical solutions in robotics.
Research Focus
Key Achievements
Top Papers
- 1