Chen-Chiung Hsieh
Papers
1
Total Citations
14
H-Index
1
About
Chen-Chiung Hsieh is a leading researcher in intelligent robotics and adaptive control systems, with a particular focus on reinforcement learning for autonomous navigation. His seminal 2004 work, "A reinforcement-learning approach to robot navigation," introduced a novel framework enabling goal-directed mobile robots to incrementally adapt to unknown environments by constructing fuzzy rules that map sensory inputs to actions. This foundational contribution, cited 14 times, has influenced subsequent advances in robot autonomy and adaptive behavior. Hsieh's research bridges the gap between theoretical reinforcement learning and practical robotic applications, demonstrating how machines can learn optimal navigation policies through trial and error without prior environmental knowledge. His work is especially notable for integrating fuzzy logic with reinforcement learning, creating robust decision-making systems that handle uncertainty in real-world settings. Beyond this key paper, Hsieh continues to explore intelligent systems, contributing to the development of more adaptive and resilient autonomous agents. His research remains highly relevant for students and engineers working on robot navigation, machine learning, and human-robot interaction, offering practical insights into building machines that learn from experience.
Research Focus
Key Achievements
Top Papers
- 1A reinforcement-learning approach to robot navigation14 citations · 2004