Dong Han
Papers
1
Total Citations
210
H-Index
1
About
Dong Han is a leading researcher at the intersection of artificial intelligence and robotics, with a primary focus on deep reinforcement learning for robotic manipulation. His most influential work, the widely cited 2023 survey “A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation,” has garnered over 210 citations, establishing itself as a key reference in the field. In this comprehensive review, Han systematically maps the landscape of deep reinforcement learning algorithms applied to core manipulation challenges—including grasping, object manipulation, and dexterous control—providing researchers with a critical taxonomy of methods, benchmarks, and open problems. Beyond this seminal survey, Han’s research contributions have advanced the practical deployment of reinforcement learning in real-world robotic systems, bridging the gap between simulation and physical environments. His work is recognized for its clarity and impact, helping to guide a new generation of researchers toward more sample-efficient, robust, and generalizable manipulation policies. Han continues to shape the field through both foundational reviews and novel algorithmic developments, making him a valuable voice for anyone exploring how intelligent robots can learn to interact with the physical world.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation210 citations · 2023