Xian Yao Ng
Papers
1
Total Citations
4
H-Index
1
About
Xian Yao Ng is a robotics researcher focused on bridging the gap between simulation and real-world deployment, particularly in visual manipulation tasks. Their most-cited work, "Real2Sim or Sim2Real: Robotics Visual Insertion Using Deep Reinforcement Learning and Real2Sim Policy Adaptation" (2023), tackles a critical challenge in robotics: transferring policies trained in simulation to physical environments. By introducing a novel Real2Sim adaptation framework, Ng demonstrates how deep reinforcement learning can achieve precise visual insertion tasks—such as peg-in-hole assembly—without requiring extensive real-world data. This contribution has already garnered 4 citations, signaling early impact in the field of sim-to-real transfer. Ng’s research lies at the intersection of computer vision, reinforcement learning, and robotic control, offering practical solutions for industrial automation and autonomous systems. Their work is particularly notable for addressing the "reality gap" that often hinders the deployment of simulated policies, making it a valuable resource for researchers exploring robust, generalizable robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1