Shih-Yeh Chen
Papers
1
Total Citations
7
H-Index
1
About
Shih-Yeh Chen is a researcher at the forefront of intelligent robotics and autonomous systems, with a particular focus on integrating deep reinforcement learning into real-world navigation and exploration tasks. His most-cited work, "Deep Reinforcement Learning-Based Robot Exploration for Constructing Map of Unknown Environment" (2021), has garnered 7 citations and stands as a key contribution to the field. In this study, Chen developed a novel framework that enables robots to autonomously explore uncharted environments and build accurate maps by learning optimal exploration policies through trial-and-error interactions. This approach addresses critical challenges in robotics, such as balancing exploration efficiency with map accuracy, and has implications for applications ranging from search-and-rescue missions to planetary exploration. Chen’s research demonstrates how reinforcement learning can bridge the gap between simulation and physical deployment, offering scalable solutions for robots operating in dynamic, unknown spaces. His work is notable for its practical emphasis on real-time decision-making and environmental adaptability, making it a valuable reference for students and researchers working on autonomous navigation, simultaneous localization and mapping (SLAM), and intelligent agent design.
Research Focus
Key Achievements
Top Papers
- 1