Yaogao Shen
Papers
1
Total Citations
11
H-Index
1
About
Yaogao Shen is a researcher in robotics and intelligent control systems, with a focus on solving complex kinematic challenges in autonomous manipulation. Their most-cited work, "A Distributed Reward Algorithm for Inverse Kinematics of Arm Robot" (2020, 11 citations), addresses a critical bottleneck in robotics: the inefficiency of traditional analytical and numerical methods for inverse kinematics in robots with complex structures. Shen proposed a novel distributed reward framework that leverages reinforcement learning to reduce the time and manual tuning required in real-world deployment, offering a more adaptive and scalable approach to continuous state-action problems. This contribution is particularly impactful for industrial and service robotics, where precise arm control is essential. Though early in their career, Shen’s work signals a shift toward data-driven, reward-based solutions for robotic motion planning. Their research bridges the gap between theoretical control algorithms and practical robotic applications, making it a valuable reference for students and engineers working on autonomous systems, human-robot interaction, and intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
- 1A Distributed Reward Algorithm for Inverse Kinematics of Arm Robot11 citations · 2020