Yun-Chien Chen
Papers
3
Total Citations
43
H-Index
3
About
Yun-Chien Chen is a robotics researcher focused on human-robot interaction, multi-agent systems, and autonomous navigation. His work bridges the gap between theoretical algorithms and real-world deployment, with particular emphasis on making robots more accessible and reliable in everyday environments. Chen’s most cited paper, “Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications” (2023, 35 citations), introduces a voice-controlled robot system that uses a deep neural network to interpret spoken commands without requiring a physical microphone—a practical innovation for service robotics. He also advances multi-agent coordination through his 2024 paper on hybrid centralized training and decentralized execution reinforcement learning, which reduces collision risks in physical robot training by simulating path-finding tasks. Additionally, his 2023 work on 3D LiDAR SLAM-based systems integrates lightweight, ground-optimized LiDAR odometry for object detection and navigation, improving mobile robot autonomy in complex environments. Chen’s research is notable for its applied focus, addressing real-world constraints like safety and usability, and his contributions are shaping the next generation of intelligent, interactive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3