Stanley Qing Shui Loh
Papers
1
Total Citations
13
H-Index
1
About
Stanley Qing Shui Loh is a rising researcher at the intersection of robotics and artificial intelligence, with a primary focus on deep reinforcement learning and its application to physical robotic systems. His most cited work, "Customisable Control Policy Learning for Robotics" (2019), directly tackles a critical bottleneck in the field: the immense data and time required to train physical robots. Loh’s major contribution lies in developing algorithms that integrate deep neural networks with traditional reinforcement learning to create more sample-efficient and adaptable control policies. By addressing the "large" data demands of real-world robots, his research paves the way for more practical and flexible autonomous systems. With 13 citations on this foundational paper, his work is gaining traction among researchers seeking to bridge the gap between simulation and reality. Loh’s focus on customisability is particularly notable, as it moves beyond one-size-fits-all solutions toward robots that can be tailored to specific tasks and environments. For students and researchers in robotics, his work represents a crucial step toward making deep reinforcement learning a viable tool for real-world, embodied intelligence.
Research Focus
Key Achievements
Top Papers
- 1Customisable Control Policy Learning for Robotics13 citations · 2019