Cong Yao
Papers
4
Total Citations
65
H-Index
3
About
Cong Yao is a leading researcher in intelligent robotics, specializing in multi-robot collaborative exploration, redundant manipulator control, and robot skill learning. His most influential work, the HMS-RRT algorithm (2024, 33 citations), introduces a hybrid multi-strategy rapidly-exploring random tree approach that dramatically improves the efficiency of multi-robot exploration in unknown environments—a critical advancement for search-and-rescue and autonomous mapping applications. Yao also made significant contributions to precision manufacturing with his inverse kinematics and planning/control co-design method for redundant manipulators (2022, 27 citations), which enables high-accuracy operations through integrated design and control optimization. His MT-RSL framework (2024, 4 citations) further pushes boundaries by introducing multitasking-oriented robot skill learning based on continuous dynamic movement primitives, enhancing both efficiency and quality in intelligent operations. Most recently, his TS-RIL framework (2025) tackles real-world imitation learning challenges by combining motion trajectory learning with obstacle avoidance. With over 65 citations across his core publications, Yao’s work bridges theoretical innovation and practical deployment, establishing him as a rising authority in autonomous robotics and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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