De-Yuan Huang
Papers
1
Total Citations
14
H-Index
1
About
De-Yuan Huang is a pioneering researcher in intelligent robotics and autonomous navigation, with a focus on reinforcement learning for adaptive systems. His most-cited work, "A reinforcement-learning approach to robot navigation" (2004), introduces a novel framework that enables mobile robots to incrementally adapt to unknown environments through fuzzy rule-based learning. By mapping sensory inputs to actions via reinforcement signals, Huang’s approach allows robots to autonomously refine their navigation strategies without prior environmental knowledge—a critical advancement for real-world deployment. This work, with 14 citations, laid foundational principles for integrating fuzzy logic with reinforcement learning in robotics. Huang’s contributions are particularly notable for bridging theoretical machine learning with practical robotic control, offering a scalable solution for goal-directed navigation in dynamic settings. His research continues to influence fields such as autonomous vehicles, service robotics, and adaptive control systems, where his methods for incremental environmental adaptation remain highly relevant. Huang’s work exemplifies how reinforcement learning can empower robots to learn from interaction, making him a key figure in the evolution of intelligent, self-improving robotic systems.
Research Focus
Key Achievements
Top Papers
- 1A reinforcement-learning approach to robot navigation14 citations · 2004