Samuel Cheng
Papers
1
Total Citations
210
H-Index
1
About
Samuel Cheng 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 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, Cheng systematically maps the landscape of deep RL approaches—from grasping to complex object manipulation—providing a critical analysis of algorithmic advances and their practical applications. His contributions have helped bridge the gap between theoretical reinforcement learning methods and real-world robotic systems, offering researchers and practitioners a structured roadmap for tackling manipulation challenges. Beyond this landmark survey, Cheng's work continues to explore how autonomous agents can learn dexterous skills through trial-and-error interaction, pushing the boundaries of what robots can achieve in unstructured environments. His research is particularly valued for its clarity in distilling complex algorithmic developments into actionable insights, making him a sought-after voice in both academic and industrial robotics communities.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation210 citations · 2023