Yew Wee Wong

Papers

Total Citations

H-Index

About

Dr. Yew Wee Wong is a robotics researcher whose work bridges cloud computing and intelligent manipulation systems. His primary research areas include cloud-enhanced robotics, deep reinforcement learning, and autonomous grasping in cluttered environments. Dr. Wong’s most cited work, “Development of a cloud-enhanced investigative mobile robot” (2016, 7 citations), pioneered the integration of cloud computing paradigms with mobile robotics, demonstrating how networked and individual robotic operations can benefit from scalable, offloaded processing—a foundational contribution to the emerging field of cloud robotics. More recently, his 2024 paper “Dual-Critic Deep Reinforcement Learning for Push-Grasping Synergy in Cluttered Environment” (2 citations) introduces a novel double-critic framework that optimizes the delicate balance between pushing and grasping actions, directly addressing the inefficiencies of redundant motion in dense spaces. This work advances practical robotic manipulation for real-world applications like warehouse automation and disaster response. Though early in its impact, the dual-critic approach represents a significant step toward more adaptive and efficient robotic systems. Dr. Wong’s research continues to shape how robots perceive and interact with complex, unstructured environments.